{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>000700</td>\n",
       "      <td>36047000700</td>\n",
       "      <td>7</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>176774</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>POLYGON ((-74.00154 40.69279, -74.00132 40.693...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>000900</td>\n",
       "      <td>36047000900</td>\n",
       "      <td>9</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>163469</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>333</td>\n",
       "      <td>306</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>POLYGON ((-73.99405 40.69090, -73.99374 40.691...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>001100</td>\n",
       "      <td>36047001100</td>\n",
       "      <td>11</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>168507</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>POLYGON ((-73.99073 40.69305, -73.99045 40.693...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>001300</td>\n",
       "      <td>36047001300</td>\n",
       "      <td>13</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>293167</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>419</td>\n",
       "      <td>398</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>POLYGON ((-73.99141 40.69863, -73.99131 40.699...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>002000</td>\n",
       "      <td>36047002000</td>\n",
       "      <td>20</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>154138</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>POLYGON ((-74.01867 40.64741, -74.01809 40.647...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20 NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        36        047    000700  36047000700      7  Census Tract   G5020   \n",
       "1        36        047    000900  36047000900      9  Census Tract   G5020   \n",
       "2        36        047    001100  36047001100     11  Census Tract   G5020   \n",
       "3        36        047    001300  36047001300     13  Census Tract   G5020   \n",
       "4        36        047    002000  36047002000     20  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20  ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S   176774         0  ...        0        0        0        0   \n",
       "1          S   163469         0  ...        0        0        0        0   \n",
       "2          S   168507         0  ...        0        0        0        0   \n",
       "3          S   293167         0  ...        0        0        0        0   \n",
       "4          S   154138         0  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0       58       49        0        9   \n",
       "1        0      333      306        0       27   \n",
       "2        0       23        0        0       23   \n",
       "3        0      419      398        0       21   \n",
       "4        0      134        0        0      134   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-74.00154 40.69279, -74.00132 40.693...  \n",
       "1  POLYGON ((-73.99405 40.69090, -73.99374 40.691...  \n",
       "2  POLYGON ((-73.99073 40.69305, -73.99045 40.693...  \n",
       "3  POLYGON ((-73.99141 40.69863, -73.99131 40.699...  \n",
       "4  POLYGON ((-74.01867 40.64741, -74.01809 40.647...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/ny_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd938fd1-bf94-4c8a-bb4b-2b902e65e6a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 5411 popn tracts for NYup\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"NYup\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population NYCLI voter-age population\n",
      "state pop= 20201249 VAP pct Hispanic is  0.18186607708102898\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP NYCLI\n",
      "total state pop= 20201249 , VAP pct Black is  0.14463236416153893\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for NYCLI\n"
     ]
    },
    {
     "data": {
      "image/png": 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jS6mATyS5N6NTUAGcV1WHYBTEwLnDeOc+jtnKC//RrbTtAZN9/Z+/zxAU/wF859Qqn9svM/rN+5iLknwhyT8leeMw1rmPSb2PlrsPgDcCh6vq0bGxqW+PWQinBU+V1EWSVwAfAd5VVd8A/hz4buC1wCFGU2eYu6cOvb6hql4HvAW4Icmb5lm3cx9k9AXxtwF/NwytxO0xn1Ope9l7SvJu4FngQ8PQIeDCqroM+DXgb5O8ir59TPJ9tOzbA3g7L/wFbkm2xyyE04o4VVKSlzIKpg9V1UcBqupwVT1XVf8L/AWjXZQwd08HeeGujiXvtaqeGi6PAB9jVPPhYUp/bGp/ZFi9bR+DtwD3VdVhWJnbYzDJ1//5+yQ5A3g1i99tddqSbAPeCvz8sGuIYTfY08PyvYw+q/kemvYx4ffRcm+PM4CfBm4/NrZU22MWwqn9qZKGfasfBB6uqveNja8bW+2ngGNHyuwGtg5HuFwEbATuGXbZfDPJ64fH/CXgjiVpYlTvdyR55bFlRh9gPzjUu21YbdtYTS37GPOC3whX2vYYM8nXf/yxfhb45LGQmLaM/kDpbwFvq6r/HBtfm9HfiCPJxUMfX2ncxyTfR8vWx+DNwJer6vnddUu2PU72qI6OP8DVjI6Aewx493LXc4L6foTRFPaLwP3Dz9XAXwNfGsZ3A+vG7vPuoZ9HGDsCDNjE6M3+GPCnDGf5WKI+LmZ0tNEDwL5jrzWjfcd7gEeHy7M79zE8/7cDTwOvHhtrvz0Yhekh4H8Y/TZ63SRff+DljHZz7md05NXFS9jHfkafSxz7N3Ls6K6fGd5vDwD3AT/ZvI+JvY+Ws49h/C+BXzlu3SXZHp6+SJLUzizs1pMkzRjDSZLUjuEkSWrHcJIktWM4SZLaMZwkSe0YTpKkdv4PPjsXmx8EYa0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for NYCLI\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "668 2117 0.017599203489999885 nonPolygon tract no, pop, area\n",
      "1.772029100001415e-05\n",
      "0.01758148319899987\n",
      "1655 2382 0.007483559311000001 nonPolygon tract no, pop, area\n",
      "0.005469334573999986\n",
      "8.5032115000003e-06\n",
      "3.12293099999656e-06\n",
      "0.0008542772130000359\n",
      "3.4989656999994264e-05\n",
      "3.922327800000866e-05\n",
      "0.0010741084464999792\n",
      "1656 1633 0.013914949535000085 nonPolygon tract no, pop, area\n",
      "0.0007168596070000174\n",
      "1.0955643999991814e-05\n",
      "0.00018290268900003233\n",
      "0.01227629610750006\n",
      "0.0007279354874999836\n",
      "1658 2351 0.0184604741209998 nonPolygon tract no, pop, area\n",
      "9.311173349998023e-05\n",
      "0.018367362387499822\n",
      "1898 1675 0.0003607014640000017 nonPolygon tract no, pop, area\n",
      "1.8426150000023423e-07\n",
      "0.0003605172025000015\n",
      "2678 0 8.138234999995787e-06 nonPolygon tract no, pop, area\n",
      "2.019172000000323e-06\n",
      "6.119062999995463e-06\n",
      "2860 2476 0.013573784286500107 nonPolygon tract no, pop, area\n",
      "0.005434501027000043\n",
      "0.008139283259500063\n",
      "3032 2947 0.0006053421529999931 nonPolygon tract no, pop, area\n",
      "8.725684600001756e-05\n",
      "0.0005180853069999756\n",
      "3501 3985 0.014661514379499889 nonPolygon tract no, pop, area\n",
      "1.140133199998478e-05\n",
      "0.014650113047499904\n",
      "4949 498 0.0017131703064997526 nonPolygon tract no, pop, area\n",
      "0.0006981804384998428\n",
      "0.0010149898679999098\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "69edb6b2-7b88-43ca-8017-ff4d271ce3e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For NY, we'll convex-hull these scattered minor areas\n",
    "convexList = [1656, 1658, 1655, 668, 4949, 3032, 1898, 3501, 2860, 2678]\n",
    "for c in range(len(convexList)):\n",
    "    m = convexList[c]\n",
    "    plotPoly(tractGeom[m])\n",
    "    tractGeom[m] = tractGeom[m].convex_hull\n",
    "    plotPoly(tractGeom[m])\n",
    "    x = tractGeom[m].centroid.x\n",
    "    y = tractGeom[m].centroid.y\n",
    "    plt.text(x+0.03,y+0.03,m,fontsize=9)\n",
    "    notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PREIPIE</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 12-9</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-8218479.283 5265873.297, -8218067.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-1</td>\n",
       "      <td>57</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-8216488.778 5256926.035, -8216484.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-2</td>\n",
       "      <td>248</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-8214177.005 5257088.554, -8214188.6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-3</td>\n",
       "      <td>368</td>\n",
       "      <td>71</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-8214253.148 5257026.663, -8214220.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-4</td>\n",
       "      <td>349</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-8212448.658 5257024.999, -8212504.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP  COUNTY     PRECINCT  G20PREDBID  G20PRERTRU  G20PRELJOR  \\\n",
       "0      36      001  Albany  Albany 12-9          30          26           1   \n",
       "1      36      001  Albany   Albany 1-1          57          20           1   \n",
       "2      36      001  Albany   Albany 1-2         248          38           2   \n",
       "3      36      001  Albany   Albany 1-3         368          71           7   \n",
       "4      36      001  Albany   Albany 1-4         349          60           3   \n",
       "\n",
       "   G20PREGHAW  G20PREIPIE  G20PREOWRI  \\\n",
       "0           0           0           0   \n",
       "1           0           0           0   \n",
       "2           3           0           1   \n",
       "3           7           0           0   \n",
       "4           2           3           0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-8218479.283 5265873.297, -8218067.0...  \n",
       "1  POLYGON ((-8216488.778 5256926.035, -8216484.4...  \n",
       "2  POLYGON ((-8214177.005 5257088.554, -8214188.6...  \n",
       "3  POLYGON ((-8214253.148 5257026.663, -8214220.3...  \n",
       "4  POLYGON ((-8212448.658 5257024.999, -8212504.0...  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/ny_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PREIPIE</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 12-9</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-73.82786 42.69639, -73.82415 42.694...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-1</td>\n",
       "      <td>57</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-73.80997 42.63729, -73.80994 42.637...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-2</td>\n",
       "      <td>248</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-73.78921 42.63837, -73.78931 42.638...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-3</td>\n",
       "      <td>368</td>\n",
       "      <td>71</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-73.78989 42.63796, -73.78960 42.638...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>001</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Albany 1-4</td>\n",
       "      <td>349</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-73.77368 42.63795, -73.77418 42.636...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP  COUNTY     PRECINCT  G20PREDBID  G20PRERTRU  G20PRELJOR  \\\n",
       "0      36      001  Albany  Albany 12-9          30          26           1   \n",
       "1      36      001  Albany   Albany 1-1          57          20           1   \n",
       "2      36      001  Albany   Albany 1-2         248          38           2   \n",
       "3      36      001  Albany   Albany 1-3         368          71           7   \n",
       "4      36      001  Albany   Albany 1-4         349          60           3   \n",
       "\n",
       "   G20PREGHAW  G20PREIPIE  G20PREOWRI  \\\n",
       "0           0           0           0   \n",
       "1           0           0           0   \n",
       "2           3           0           1   \n",
       "3           7           0           0   \n",
       "4           2           3           0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-73.82786 42.69639, -73.82415 42.694...  \n",
       "1  POLYGON ((-73.80997 42.63729, -73.80994 42.637...  \n",
       "2  POLYGON ((-73.78921 42.63837, -73.78931 42.638...  \n",
       "3  POLYGON ((-73.78989 42.63796, -73.78960 42.638...  \n",
       "4  POLYGON ((-73.77368 42.63795, -73.77418 42.636...  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15376 0.3827282310466085 8496883 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#see NJ code for convex-hulling tracts\n",
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        plotPoly(vtdGeom[p])\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "isSkippedTract = [0]*nTracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "eb241d56-c85d-46c9-9e7e-1deb5b86cb5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downstate Pop =  7771268.0 which is  10.002  districts' worth\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ID tracts that are in DOWNstate NY\n",
    "nDistricts = 26\n",
    "isUpstateTract = [0] * nTracts\n",
    "isUpstatePrecinct = [0] * nPrecincts\n",
    "avgDistrictPop = np.sum(tractPop)/nDistricts\n",
    "#let's visualize north of NYC\n",
    "sumUpstatePop = 0.\n",
    "xCut = -74.\n",
    "yCut = 41.01\n",
    "slope = 0.3\n",
    "pt1 = Point(xCut - 0.5,yCut - 0.5*slope)\n",
    "pt2 = Point(xCut + 0.8, yCut + 0.8*slope)\n",
    "cutLine = LineString([pt1,pt2])\n",
    "x,y = cutLine.xy\n",
    "plt.plot(x,y)\n",
    "for m in range(nTracts):\n",
    "    x = tractGeom[m].centroid.x\n",
    "    y = tractGeom[m].centroid.y\n",
    "    if (y - yCut) > slope * (x - xCut) :\n",
    "        sumUpstatePop += tractPop[m]\n",
    "        isUpstateTract[m] = 1\n",
    "        if (y - yCut ) > -5 + slope * (x - xCut):  #for zooming in on White Plains, change \"5\" to 0.2\n",
    "            plotPoly(tractGeom[m])\n",
    "print(\"downstate Pop = \",sumUpstatePop,\"which is \",round(sumUpstatePop/avgDistrictPop,4),\" districts' worth\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ac461040-0ec3-4da6-90c1-6ec1c5072cfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upstate Voters =  3697218.0 which is about 11.3133  districts worth\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now ID the upstate VTDs\n",
    "sumUpstateVoters = 0.   #not needed, just curious here\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if (y - yCut) > slope * (x - xCut) :\n",
    "        sumUpstateVoters += vtdTrump[p] + vtdBiden[p]\n",
    "        isUpstatePrecinct[p] = 1\n",
    "        if (y - yCut ) > -5 + slope * (x - xCut):  #for zooming in on White Plains, change \"5\" to 0.2\n",
    "            plotPoly(vtdGeom[p])\n",
    "print(\"upstate Voters = \",sumUpstateVoters,\"which is about\",round(nDistricts*sumUpstateVoters/\n",
    "      (np.sum(vtdTrump)+np.sum(vtdBiden)), 4),\" districts worth\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "faa842d7-ba32-41f4-8218-865b15427ff6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[127, 128, 405, 732, 769, 788, 1393, 2257, 2804, 3250, 3692, 3786, 4640, 4898, 4920, 5237]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag coastal low-population tracts.\n",
    "lakeTracts = [-99999]\n",
    "minTractPop = 50\n",
    "for m in range(nTracts):\n",
    "    if isUpstateTract[m] == 1 and tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        if tractGeom[m].area > 0.01:  #only show tract numbers for larger tracts\n",
    "            plt.text(x, y-0.05,m, fontsize=9)\n",
    "       \n",
    "        if x < -76 : #only worry about Lake tracts\n",
    "            if lakeTracts == [-99999]:\n",
    "                lakeTracts = [m]\n",
    "                # isSkippedTract[m] = 1   #not yet\n",
    "            else:\n",
    "                if m==99999:   #could place a restriction here\n",
    "                    thisTract = \"interior\"\n",
    "                else:\n",
    "                    lakeTracts.append(m)\n",
    "                    # isSkippedTract[m] = 1   #notyet\n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "06852664-90ce-4851-a459-246f97ee245c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Flag these bigger lake tracts\n",
    "skippedLakeTracts = [127,732,788,1393,2257,3250,3692,4898,5237]\n",
    "for m in skippedLakeTracts:\n",
    "    x = tractGeom[m].centroid.x\n",
    "    y = tractGeom[m].centroid.y\n",
    "    #plotPoly(tractGeom[m])\n",
    "    plt.text(x, y,m, fontsize=9)\n",
    "    isSkippedTract[m]=1\n",
    "for m in lakeTracts:\n",
    "    plotPoly(tractGeom[m])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "df63628e-0ef6-45db-9ab8-e90844f95c02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#zoom in near Watertown\n",
    "Watertown = Point(-76.2, 43.8)\n",
    "for m in range(nTracts):\n",
    "    if isUpstateTract[m] == 1 and tractPop[m] > minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if tractGeom[m].distance(Watertown) < 0.7:\n",
    "            plotPoly(tractGeom[m])\n",
    "            plt.text(x,y,m,fontsize=6)\n",
    "x,y = tractGeom[732].exterior.xy\n",
    "#plt.plot(x,y,color=\"blue\")\n",
    "pt1 = Point(-76.6,43.7)\n",
    "pt2 = Point(-76.15,43.7)\n",
    "pt3 = Point(-75.9,44.4)\n",
    "pt4 = Point(-76.6,44.4)\n",
    "cutLine = LineString([pt2,pt3])\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "plotPoly(clipPoly)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  9 tracts w pop 18931.0  to reassign to tracts...\n",
      "[142, 151, 533, 664, 666, 1356, 1650]\n",
      "I have flagged  23 precincts to reassign to precincts...\n",
      "[2368, 2373, 2374, 2379, 2381, 2384, 2388, 2397, 2402, 2409, 2417, 2420, 2422, 11681, 11682, 11755, 11761, 12810]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractPop[t] > 10:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1: # and vtdGeom[p].centroid.x > -76.4 and vtdGeom[p].centroid.y > 38.4:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  18931.0 people (VAP of 15473.0 ) and  15959.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first upstate tract is  34\n"
     ]
    }
   ],
   "source": [
    "#ok, what's the first upstate tract?\n",
    "firstTract = -1\n",
    "for t in range(nTracts):\n",
    "    if isUpstateTract[t] == 1:\n",
    "        if firstTract == -1 :\n",
    "            print(\"first upstate tract is \",t)\n",
    "            firstTract = t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "5ed3aeb1-bd4e-4282-83d1-6af29983db59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 100\n",
      "working on tract 200\n",
      "working on tract 300\n",
      "working on tract 400\n",
      "working on tract 500\n",
      "working on tract 600\n",
      "working on tract 700\n",
      "working on tract 800\n",
      "working on tract 900\n",
      "working on tract 1000\n",
      "working on tract 1100\n",
      "working on tract 1200\n",
      "working on tract 1300\n",
      "working on tract 1400\n",
      "working on tract 1500\n",
      "working on tract 1600\n",
      "working on tract 1700\n",
      "working on tract 1800\n",
      "working on tract 1900\n",
      "working on tract 2000\n",
      "working on tract 2100\n",
      "working on tract 2200\n",
      "working on tract 2300\n",
      "working on tract 2400\n",
      "working on tract 2500\n",
      "working on tract 2600\n",
      "working on tract 2700\n",
      "working on tract 2800\n",
      "working on tract 2900\n",
      "working on tract 3000\n",
      "working on tract 3100\n",
      "working on tract 3200\n",
      "working on tract 3300\n",
      "working on tract 3400\n",
      "working on tract 3500\n",
      "working on tract 3600\n",
      "working on tract 3700\n",
      "working on tract 3800\n",
      "working on tract 3900\n",
      "working on tract 4000\n",
      "working on tract 4100\n",
      "working on tract 4200\n",
      "working on tract 4300\n",
      "working on tract 4400\n",
      "working on tract 4500\n",
      "working on tract 4600\n",
      "working on tract 4700\n",
      "working on tract 4800\n",
      "working on tract 4900\n",
      "working on tract 5000\n",
      "working on tract 5100\n",
      "working on tract 5200\n",
      "working on tract 5300\n",
      "working on tract 5400\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, build the upstate NY map\n",
    "#build the initial (uncut) state map out of the TRACTS, excluding the clipPoly and lakeshore tracts\n",
    "tractMAP = tractGeom[firstTract]\n",
    "for t in range(firstTract+1,nTracts):\n",
    "    if t%100 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0 and isUpstateTract[t] ==1:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ad14ca84-d3ba-47a5-a4d3-47dd4a9e305c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have saved whole-state data. upstatePop =  7771265 777126.5 (per district), with pctR of 0.47692503683203646\n"
     ]
    }
   ],
   "source": [
    "#Now, let's create a csv that identifies whether tracts and precincts are upstate or downstate\n",
    "#then, zero out the tractPop and vtdBiden, vtdTrump of the upstate areas\n",
    "tractNo = [0]*nTracts\n",
    "precinctNo = [0]*nPrecincts\n",
    "for t in range(nTracts):\n",
    "    tractNo[t] = t\n",
    "df = pd.DataFrame( {\"tractNo\": tractNo,\"isUpstate\":isDownstateTract,\"tractPop\":tractPop,\n",
    "                    \"tractVAP\":tractVAP,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack} ) \n",
    "\n",
    "outname = \"upstateNY_tract_input.csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "for t in range(nTracts):\n",
    "    if isUpstateTract[t] == 0 : #zero these out for this upstateNY submap\n",
    "        tractPop[t] = 0.\n",
    "        tractVAP[t] = 0.\n",
    "        tractHisp[t] = 0.\n",
    "        tractBlack[t] = 0.\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[t] = p\n",
    "df2 = pd.DataFrame( {\"precinctNo\": precinctNo,\"isUpstate\":isDownstatePrecinct,\n",
    "                    \"vtdTrump\":vtdTrump,\"vtdBiden\":vtdBiden} ) \n",
    "\n",
    "outname = \"upstateNY_precinct_input.csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df2.to_csv(outpath)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    if isUpstatePrecinct[p] == 0:  #zero these out for this upstateNY submap\n",
    "        vtdTrump[p] = 0\n",
    "        vtdBiden[p] = 0\n",
    "\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump)+np.sum(vtdBiden) )\n",
    "statePop = np.sum(tractPop)\n",
    "nDistricts = 10\n",
    "avgDistrictPop = statePop/nDistricts\n",
    "print(\"I have saved whole-state data. upstatePop = \",statePop,avgDistrictPop,\"(per district), with pctR of\",stateGOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?  #upstateNY only\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.4642857142857143 56\n",
      "1000 0.5380281690140845 710\n",
      "2000 0.0375 80\n",
      "3000 0.4320388349514563 618\n",
      "4000 0.0 0\n",
      "5000 0.0 0\n",
      "6000 0.0 0\n",
      "7000 0.0 0\n",
      "8000 0.0 0\n",
      "9000 0.0 0\n",
      "10000 0.0 0\n",
      "11000 0.36666666666666664 120\n",
      "12000 0.7048832271762208 471\n",
      "13000 0.0 0\n",
      "14000 0.5691937424789411 831\n",
      "15000 0.0 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0 and isUpstatePrecinct[p]==1: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%1000 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 13.37020004490353 13.37020004490353\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "wholeMAP = tractMAP #wholeMAP    # normally keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",wholeMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "#x2, y2 = origMAP.exterior.xy\n",
    "#plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  7771265 3697189 0.47692503683203646\n",
      "compare to Census 20201249 Leip has Trump + Biden = 8496883.0 0.3827282310466085 for whole state\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these will be for WHOLE STATE\n",
    "censusPop = 20201249\n",
    "atlasTrump = 3251997.\n",
    "atlasBiden = 5244886\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden),\"for whole state\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 7771265 7771265.0\n",
      "state Trumpers before, after slice opn are 1763282 1763282.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "b5334311-b597-4568-88c7-eb3c9d4bdf09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 60 grids for 10 districts\n",
      "starting grid no 0 at x = -79.43594959999999, y = 41.349708 \n",
      "starting grid no 5 at x = -79.43594959999999, y = 44.682578 \n",
      "starting grid no 10 at x = -78.7838908, y = 44.016003999999995 \n",
      "starting grid no 15 at x = -78.131832, y = 43.34942999999999 \n",
      "starting grid no 20 at x = -77.47977320000001, y = 42.68285600000001 \n",
      "starting grid no 25 at x = -76.8277144, y = 42.016282000000004 \n",
      "starting grid no 30 at x = -76.1756556, y = 41.349708 \n",
      "starting grid no 35 at x = -76.1756556, y = 44.682578 \n",
      "starting grid no 40 at x = -75.52359679999999, y = 44.016003999999995 \n",
      "starting grid no 45 at x = -74.871538, y = 43.34942999999999 \n",
      "starting grid no 50 at x = -74.21947920000001, y = 42.68285600000001 \n",
      "starting grid no 55 at x = -73.5674204, y = 42.016282000000004 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 425703.5756812821 2083725.6937227903 42.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 10   #upstate only\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 2.5  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "e9fe7b11-da40-4de7-b0b9-42b94aa6e16d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2242 6671\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(isUpstateTract),np.sum(isUpstatePrecinct) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.78263033273482e-09 2.4046084999939926e-06\n",
      "grid 0 is empty\n",
      "grid 3 is empty\n",
      "grid 4 is empty\n",
      "grid 5 is empty\n",
      "grid 6 is empty\n",
      "grid 10 is empty\n",
      "grid 11 is empty\n",
      "grid 12 is empty\n",
      "grid 16 is empty\n",
      "grid 17 is empty\n",
      "grid 18 is empty\n",
      "grid 22 is empty\n",
      "grid 23 is empty\n",
      "grid 24 is empty\n",
      "grid 28 is empty\n",
      "grid 29 is empty\n",
      "grid 30 is empty\n",
      "grid 36 is empty\n"
     ]
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop\n",
    "isEmptyGrid = [0]*nGrids  #to speed main code, flag the grids that are empty\n",
    "for nG in range(nGrids):\n",
    "    if gridPop[nG] == 0:\n",
    "        isEmptyGrid[nG] = 1\n",
    "        print(\"grid\",nG,\"is empty\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 5411 tracts\n",
      "I am working on tract number 20 of 5411 tracts\n",
      "I am working on tract number 40 of 5411 tracts\n",
      "I am working on tract number 60 of 5411 tracts\n",
      "I am working on tract number 80 of 5411 tracts\n",
      "I am working on tract number 100 of 5411 tracts\n",
      "I am working on tract number 120 of 5411 tracts\n",
      "I am working on tract number 140 of 5411 tracts\n",
      "I am working on tract number 160 of 5411 tracts\n",
      "I am working on tract number 180 of 5411 tracts\n",
      "I am working on tract number 200 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 202 2.0 2 90.0 1.0679 219494.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 202 3.0 2 90.0 0.9731 219494.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 204 3.0 1 48.0 0.7623 716590.4\n",
      "I am working on tract number 220 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 222\n",
      "we have 2 non-opposing shorted wedges for tract no 226\n",
      "7 yoyos for tract,wedge,wedgePop,r= 231 2 551849.8090200422 1.8867\n",
      "8 yoyos for tract,wedge,wedgePop,r= 231 2 134584.80625327863 0.9434\n",
      "9 yoyos for tract,wedge,wedgePop,r= 231 2 550749.1138853322 1.8688\n",
      "10 yoyos for tract,wedge,wedgePop,r= 231 2 324314.8679466518 1.4061\n",
      "11 yoyos for tract,wedge,wedgePop,r= 231 2 523643.98880449624 1.6374\n",
      "11 yoyos for tract,wedge,wedgePop,r= 231 2 479763.9625089969 1.5218\n",
      "11 yoyos for tract,wedge,wedgePop,r= 231 2 436085.20201083185 1.4639\n",
      "11 yoyos for tract,wedge,wedgePop,r= 231 2 385895.10460791236 1.435\n",
      "12 yoyos for tract,wedge,wedgePop,r= 231 2 353007.0752510487 1.4205\n",
      "13 yoyos for tract,wedge,wedgePop,r= 231 2 368637.45042052947 1.4278\n",
      "I am working on tract number 240 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 252 4.0 0 90.0 1.8736 207645.4\n",
      "I am working on tract number 260 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 275\n",
      "we have 2 non-opposing shorted wedges for tract no 276\n",
      "I am working on tract number 280 of 5411 tracts\n",
      "I am working on tract number 300 of 5411 tracts\n",
      "I am working on tract number 320 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 327 3.0 3 41.4 0.7947 361368.8\n",
      "I am working on tract number 340 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 342 7.0 0 110.6 0.8628 208633.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 342 8.0 0 110.6 0.8175 208633.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 345 4.0 1 90.0 0.9966 225267.2\n",
      "I am working on tract number 360 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 379\n",
      "I am working on tract number 380 of 5411 tracts\n",
      "I am working on tract number 400 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 411\n",
      "I am working on tract number 420 of 5411 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 423 1 291803.4137144303 1.0713\n",
      "8 yoyos for tract,wedge,wedgePop,r= 423 1 50607.458964160556 0.5356\n",
      "9 yoyos for tract,wedge,wedgePop,r= 423 1 291585.2587525514 1.0543\n",
      "10 yoyos for tract,wedge,wedgePop,r= 423 1 127731.87793090654 0.795\n",
      "11 yoyos for tract,wedge,wedgePop,r= 423 1 277587.8041716401 0.9247\n",
      "11 yoyos for tract,wedge,wedgePop,r= 423 1 242926.07689184847 0.8598\n",
      "12 yoyos for tract,wedge,wedgePop,r= 423 1 180934.5976146069 0.8274\n",
      "13 yoyos for tract,wedge,wedgePop,r= 423 1 209517.09215779498 0.8436\n",
      "I am working on tract number 440 of 5411 tracts\n",
      "I am working on tract number 460 of 5411 tracts\n",
      "I am working on tract number 480 of 5411 tracts\n",
      "I am working on tract number 500 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 512 2.0 0 90.0 1.0343 251114.2\n",
      "we have 2 non-opposing shorted wedges for tract no 516\n",
      "we have 2 non-opposing shorted wedges for tract no 517\n",
      "we have 2 non-opposing shorted wedges for tract no 519\n",
      "I am working on tract number 520 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 520\n",
      "we have 2 non-opposing shorted wedges for tract no 521\n",
      "we have 2 non-opposing shorted wedges for tract no 523\n",
      "we have 2 non-opposing shorted wedges for tract no 536\n",
      "I am working on tract number 540 of 5411 tracts\n",
      "I am working on tract number 560 of 5411 tracts\n",
      "I am working on tract number 580 of 5411 tracts\n",
      "I am working on tract number 600 of 5411 tracts\n",
      "I am working on tract number 620 of 5411 tracts\n",
      "I am working on tract number 640 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 642\n",
      "I am working on tract number 660 of 5411 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 661 1 30134.22309520206 0.5395\n",
      "8 yoyos for tract,wedge,wedgePop,r= 661 1 1001031.6936781467 1.9\n",
      "8 yoyos for tract,wedge,wedgePop,r= 661 1 591043.9890878152 1.2197\n",
      "9 yoyos for tract,wedge,wedgePop,r= 661 1 129098.54088447022 0.8796\n",
      "10 yoyos for tract,wedge,wedgePop,r= 661 1 499317.2601392366 1.0497\n",
      "11 yoyos for tract,wedge,wedgePop,r= 661 1 280433.40023417026 0.9646\n",
      "12 yoyos for tract,wedge,wedgePop,r= 661 1 406931.9332774743 1.0072\n",
      "12 yoyos for tract,wedge,wedgePop,r= 661 1 347490.1514676518 0.9859\n",
      "7 yoyos for tract,wedge,wedgePop,r= 664 1 665461.0521301612 1.3685\n",
      "8 yoyos for tract,wedge,wedgePop,r= 664 1 43800.95447061793 0.6843\n",
      "9 yoyos for tract,wedge,wedgePop,r= 664 1 662329.291402836 1.3568\n",
      "10 yoyos for tract,wedge,wedgePop,r= 664 1 284240.4250785598 1.0205\n",
      "11 yoyos for tract,wedge,wedgePop,r= 664 1 606432.1063797453 1.1887\n",
      "11 yoyos for tract,wedge,wedgePop,r= 664 1 516675.9874693304 1.1046\n",
      "11 yoyos for tract,wedge,wedgePop,r= 664 1 414756.18000758573 1.0626\n",
      "11 yoyos for tract,wedge,wedgePop,r= 664 1 342721.90670273703 1.0416\n",
      "12 yoyos for tract,wedge,wedgePop,r= 664 1 309827.422849145 1.0311\n",
      "I am working on tract number 680 of 5411 tracts\n",
      "I am working on tract number 700 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 719 3.0 0 90.0 0.5416 327102.3\n",
      "I am working on tract number 720 of 5411 tracts\n",
      "I am working on tract number 740 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 742\n",
      "we have 2 non-opposing shorted wedges for tract no 749\n",
      "we have 2 non-opposing shorted wedges for tract no 750\n",
      "we have 2 non-opposing shorted wedges for tract no 752\n",
      "we have 2 non-opposing shorted wedges for tract no 753\n",
      "I am working on tract number 760 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 769 3.0 1 47.6 0.8839 473702.1\n",
      "I am working on tract number 780 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 792 8.0 1 55.5 1.0931 219943.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 792 13.0 1 55.5 1.0802 219943.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 792 1 205001.8497551753 0.9481\n",
      "8 yoyos for tract,wedge,wedgePop,r= 792 1 117051.82972924078 0.474\n",
      "9 yoyos for tract,wedge,wedgePop,r= 792 1 210873.07971526304 0.9769\n",
      "10 yoyos for tract,wedge,wedgePop,r= 792 1 138884.05067543295 0.7255\n",
      "10 yoyos for tract,wedge,wedgePop,r= 792 1 151683.24445669336 0.8512\n",
      "10 yoyos for tract,wedge,wedgePop,r= 792 1 172943.3538557914 0.914\n",
      "11 yoyos for tract,wedge,wedgePop,r= 792 1 203483.93063166054 0.9455\n",
      "12 yoyos for tract,wedge,wedgePop,r= 792 1 187106.3473981028 0.9297\n",
      "I am working on tract number 800 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 806\n",
      "7 yoyos for tract,wedge,wedgePop,r= 808 1 807945.6739105197 1.7967\n",
      "8 yoyos for tract,wedge,wedgePop,r= 808 1 63302.87687886192 0.8983\n",
      "9 yoyos for tract,wedge,wedgePop,r= 808 1 808952.8461366177 1.7989\n",
      "9 yoyos for tract,wedge,wedgePop,r= 808 1 377913.59613140405 1.3486\n",
      "10 yoyos for tract,wedge,wedgePop,r= 808 1 102175.94507331884 1.1235\n",
      "10 yoyos for tract,wedge,wedgePop,r= 808 1 156892.96145837448 1.236\n",
      "10 yoyos for tract,wedge,wedgePop,r= 808 1 279005.0131442548 1.2923\n",
      "10 yoyos for tract,wedge,wedgePop,r= 808 1 329253.5016585862 1.3204\n",
      "11 yoyos for tract,wedge,wedgePop,r= 808 1 350494.92072725383 1.3345\n",
      "I am working on tract number 820 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 821\n",
      "I am working on tract number 840 of 5411 tracts\n",
      "I am working on tract number 860 of 5411 tracts\n",
      "I am working on tract number 880 of 5411 tracts\n",
      "I am working on tract number 900 of 5411 tracts\n",
      "I am working on tract number 920 of 5411 tracts\n",
      "I am working on tract number 940 of 5411 tracts\n",
      "I am working on tract number 960 of 5411 tracts\n",
      "I am working on tract number 980 of 5411 tracts\n",
      "I am working on tract number 1000 of 5411 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1009 2 311299.93449018314 1.2983\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1009 2 30637.297523078392 0.6492\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1009 2 311238.7211284418 1.2976\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1009 2 93665.91343607617 0.9734\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1009 2 277086.1974720664 1.1355\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1009 2 154821.24833865542 1.0544\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1009 2 215011.1675109705 1.0949\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1009 2 184653.11129585665 1.0747\n",
      "15 yoyos for tract,wedge,wedgePop,r= 1009 2 199216.14842553108 1.0848\n",
      "I am working on tract number 1020 of 5411 tracts\n",
      "I am working on tract number 1040 of 5411 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1041 3 231444.01695503428 3.0808\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1041 3 110883.59146902336 1.5404\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1041 3 231232.6476655316 3.0718\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1041 3 156274.91802310036 2.3061\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1041 3 211546.78961657142 2.6889\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1041 3 163996.84771010745 2.4975\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1041 3 179760.99823609923 2.5932\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1041 3 202188.8031693105 2.6411\n",
      "I am working on tract number 1060 of 5411 tracts\n",
      "I am working on tract number 1080 of 5411 tracts\n",
      "I am working on tract number 1100 of 5411 tracts\n",
      "I am working on tract number 1120 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1121\n",
      "we have 2 non-opposing shorted wedges for tract no 1122\n",
      "we have 2 non-opposing shorted wedges for tract no 1125\n",
      "we have 2 non-opposing shorted wedges for tract no 1126\n",
      "I am working on tract number 1140 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1148\n",
      "I am working on tract number 1160 of 5411 tracts\n",
      "I am working on tract number 1180 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1183\n",
      "I am working on tract number 1200 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1214 4.0 1 52.2 0.6404 695314.9\n",
      "I am working on tract number 1220 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1228\n",
      "I am working on tract number 1240 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1244\n",
      "we have 2 non-opposing shorted wedges for tract no 1247\n",
      "we have 2 non-opposing shorted wedges for tract no 1250\n",
      "I am working on tract number 1260 of 5411 tracts\n",
      "I am working on tract number 1280 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1285 2.0 3 90.0 0.3182 253255.6\n",
      "I am working on tract number 1300 of 5411 tracts\n",
      "I am working on tract number 1320 of 5411 tracts\n",
      "I am working on tract number 1340 of 5411 tracts\n",
      "I am working on tract number 1360 of 5411 tracts\n",
      "I am working on tract number 1380 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1392\n",
      "we have 2 non-opposing shorted wedges for tract no 1394\n",
      "I am working on tract number 1400 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1406\n",
      "we have 2 non-opposing shorted wedges for tract no 1409\n",
      "I am working on tract number 1420 of 5411 tracts\n",
      "I am working on tract number 1440 of 5411 tracts\n",
      "I am working on tract number 1460 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1465 2.0 1 90.0 0.5085 263194.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1465 3.0 1 90.0 0.4011 263194.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1465 4.0 1 90.0 0.3163 263194.3\n",
      "we have 2 non-opposing shorted wedges for tract no 1466\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1470 2.0 2 90.0 0.2534 208294.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1470 3.0 2 90.0 0.2403 208294.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1471 3.0 1 41.9 0.7792 290756.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1471 4.0 1 41.9 0.6922 290756.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1471 5.0 1 41.9 0.615 290756.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1471 6.0 1 41.9 0.5464 290756.0\n",
      "I am working on tract number 1480 of 5411 tracts\n",
      "I am working on tract number 1500 of 5411 tracts\n",
      "I am working on tract number 1520 of 5411 tracts\n",
      "I am working on tract number 1540 of 5411 tracts\n",
      "I am working on tract number 1560 of 5411 tracts\n",
      "I am working on tract number 1580 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1599\n",
      "I am working on tract number 1600 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1602 8.0 0 116.2 0.4623 229291.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1602 9.0 0 116.2 0.4071 229291.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1604 3.0 1 37.3 1.1077 291191.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1604 4.0 1 37.3 1.0034 291191.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1608 5.0 1 37.8 1.1264 376295.8\n",
      "I am working on tract number 1620 of 5411 tracts\n",
      "I am working on tract number 1640 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1645\n",
      "I am working on tract number 1660 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1677 6.0 3 75.7 1.7773 227557.4\n",
      "I am working on tract number 1680 of 5411 tracts\n",
      "I am working on tract number 1700 of 5411 tracts\n",
      "I am working on tract number 1720 of 5411 tracts\n",
      "I am working on tract number 1740 of 5411 tracts\n",
      "I am working on tract number 1760 of 5411 tracts\n",
      "I am working on tract number 1780 of 5411 tracts\n",
      "I am working on tract number 1800 of 5411 tracts\n",
      "I am working on tract number 1820 of 5411 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1824 0 218600.16660855027 1.0015\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1824 0 175822.88268328347 0.5007\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1824 0 218781.30145027192 1.0032\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1824 0 184847.61307810387 0.752\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1824 0 203053.82320270446 0.8776\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1824 0 186565.78488635182 0.8148\n",
      "I am working on tract number 1840 of 5411 tracts\n",
      "I am working on tract number 1860 of 5411 tracts\n",
      "I am working on tract number 1880 of 5411 tracts\n",
      "I am working on tract number 1900 of 5411 tracts\n",
      "I am working on tract number 1920 of 5411 tracts\n",
      "I am working on tract number 1940 of 5411 tracts\n",
      "I am working on tract number 1960 of 5411 tracts\n",
      "I am working on tract number 1980 of 5411 tracts\n",
      "I am working on tract number 2000 of 5411 tracts\n",
      "I am working on tract number 2020 of 5411 tracts\n",
      "I am working on tract number 2040 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2052\n",
      "I am working on tract number 2060 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2079\n",
      "I am working on tract number 2080 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2095 7.0 1 40.0 1.6465 276179.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2095 8.0 1 40.0 1.5358 276179.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2095 9.0 1 40.0 1.4326 276179.2\n",
      "I am working on tract number 2100 of 5411 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2100 1 206654.4241435089 0.6631\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2100 1 10132.08875842503 0.3316\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2100 1 214119.87091035763 0.7021\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2100 1 49679.77725419443 0.5168\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2100 1 172720.7888091572 0.6095\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2100 1 205096.65230536574 0.6558\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2100 1 197950.24371730216 0.6326\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2100 1 189326.10604182826 0.621\n",
      "I am working on tract number 2120 of 5411 tracts\n",
      "I am working on tract number 2140 of 5411 tracts\n",
      "I am working on tract number 2160 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2175\n",
      "we have 2 non-opposing shorted wedges for tract no 2179\n",
      "I am working on tract number 2180 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2180\n",
      "we have 2 non-opposing shorted wedges for tract no 2181\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2184 3.0 3 58.4 0.6976 317841.8\n",
      "I am working on tract number 2200 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2208\n",
      "we have 2 non-opposing shorted wedges for tract no 2209\n",
      "we have 2 non-opposing shorted wedges for tract no 2210\n",
      "I am working on tract number 2220 of 5411 tracts\n",
      "I am working on tract number 2240 of 5411 tracts\n",
      "I am working on tract number 2260 of 5411 tracts\n",
      "I am working on tract number 2280 of 5411 tracts\n",
      "I am working on tract number 2300 of 5411 tracts\n",
      "I am working on tract number 2320 of 5411 tracts\n",
      "I am working on tract number 2340 of 5411 tracts\n",
      "I am working on tract number 2360 of 5411 tracts\n",
      "I am working on tract number 2380 of 5411 tracts\n",
      "I am working on tract number 2400 of 5411 tracts\n",
      "I am working on tract number 2420 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2421\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2422 0 1403525.565591984 1.956\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2422 0 144800.06759508792 0.978\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2422 0 1401723.8105713949 1.9488\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2422 0 988774.4986026273 1.4634\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2422 0 242909.97005376918 1.2207\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2422 0 537897.2507729933 1.3421\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2422 0 794391.0031346929 1.4027\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2422 0 665471.4631543383 1.3724\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2422 0 727895.0156184854 1.3876\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2422 0 760655.0372203271 1.3951\n",
      "I am working on tract number 2440 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2456 2.0 3 90.0 0.5131 292172.4\n",
      "I am working on tract number 2460 of 5411 tracts\n",
      "I am working on tract number 2480 of 5411 tracts\n",
      "I am working on tract number 2500 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2500\n",
      "we have 2 non-opposing shorted wedges for tract no 2518\n",
      "I am working on tract number 2520 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2539\n",
      "I am working on tract number 2540 of 5411 tracts\n",
      "I am working on tract number 2560 of 5411 tracts\n",
      "I am working on tract number 2580 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2596\n",
      "I am working on tract number 2600 of 5411 tracts\n",
      "I am working on tract number 2620 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2639\n",
      "I am working on tract number 2640 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2640\n",
      "we have 2 non-opposing shorted wedges for tract no 2645\n",
      "I am working on tract number 2660 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2666\n",
      "I am working on tract number 2680 of 5411 tracts\n",
      "I am working on tract number 2700 of 5411 tracts\n",
      "I am working on tract number 2720 of 5411 tracts\n",
      "I am working on tract number 2740 of 5411 tracts\n",
      "I am working on tract number 2760 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2768\n",
      "we have 2 non-opposing shorted wedges for tract no 2769\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2770 10.0 3 36.9 3.0599 448190.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2773 2.0 0 90.0 0.7759 260146.0\n",
      "I am working on tract number 2780 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2789\n",
      "I am working on tract number 2800 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2809\n",
      "we have 2 non-opposing shorted wedges for tract no 2813\n",
      "I am working on tract number 2820 of 5411 tracts\n",
      "I am working on tract number 2840 of 5411 tracts\n",
      "I am working on tract number 2860 of 5411 tracts\n",
      "I am working on tract number 2880 of 5411 tracts\n",
      "I am working on tract number 2900 of 5411 tracts\n",
      "I am working on tract number 2920 of 5411 tracts\n",
      "I am working on tract number 2940 of 5411 tracts\n",
      "I am working on tract number 2960 of 5411 tracts\n",
      "I am working on tract number 2980 of 5411 tracts\n",
      "I am working on tract number 3000 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3002\n",
      "I am working on tract number 3020 of 5411 tracts\n",
      "I am working on tract number 3040 of 5411 tracts\n",
      "I am working on tract number 3060 of 5411 tracts\n",
      "I am working on tract number 3080 of 5411 tracts\n",
      "I am working on tract number 3100 of 5411 tracts\n",
      "I am working on tract number 3120 of 5411 tracts\n",
      "I am working on tract number 3140 of 5411 tracts\n",
      "I am working on tract number 3160 of 5411 tracts\n",
      "I am working on tract number 3180 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3192 7.0 0 123.6 0.5108 212391.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3192 8.0 0 123.6 0.4774 212391.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3192 9.0 0 123.6 0.4462 212391.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3192 10.0 0 123.6 0.417 212391.0\n",
      "I am working on tract number 3200 of 5411 tracts\n",
      "I am working on tract number 3220 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3231\n",
      "I am working on tract number 3240 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3248 3.0 1 41.9 1.0529 284472.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3248 4.0 1 41.9 0.9396 284472.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3248 5.0 1 41.9 0.8385 284472.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3248 6.0 1 41.9 0.7482 284472.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3248 7.0 1 41.9 0.6677 284472.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3248 8.0 1 41.9 0.5959 284472.9\n",
      "I am working on tract number 3260 of 5411 tracts\n",
      "I am working on tract number 3280 of 5411 tracts\n",
      "I am working on tract number 3300 of 5411 tracts\n",
      "I am working on tract number 3320 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3329\n",
      "we have 2 non-opposing shorted wedges for tract no 3335\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3339 1 380542.15636412427 1.2211\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3339 1 259529.2552729728 0.6105\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3339 1 384138.9853301525 1.2433\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3339 1 303367.29561237775 0.9269\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3339 1 318676.44350388384 1.0851\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3339 1 334944.2326699506 1.1642\n",
      "I am working on tract number 3340 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3342\n",
      "we have 2 non-opposing shorted wedges for tract no 3343\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3344 6.0 0 119.9 0.5148 235537.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3344 7.0 0 119.9 0.4439 235537.6\n",
      "I am working on tract number 3360 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3361\n",
      "we have 2 non-opposing shorted wedges for tract no 3376\n",
      "I am working on tract number 3380 of 5411 tracts\n",
      "I am working on tract number 3400 of 5411 tracts\n",
      "I am working on tract number 3420 of 5411 tracts\n",
      "I am working on tract number 3440 of 5411 tracts\n",
      "I am working on tract number 3460 of 5411 tracts\n",
      "I am working on tract number 3480 of 5411 tracts\n",
      "I am working on tract number 3500 of 5411 tracts\n",
      "I am working on tract number 3520 of 5411 tracts\n",
      "I am working on tract number 3540 of 5411 tracts\n",
      "I am working on tract number 3560 of 5411 tracts\n",
      "I am working on tract number 3580 of 5411 tracts\n",
      "I am working on tract number 3600 of 5411 tracts\n",
      "I am working on tract number 3620 of 5411 tracts\n",
      "I am working on tract number 3640 of 5411 tracts\n",
      "I am working on tract number 3660 of 5411 tracts\n",
      "I am working on tract number 3680 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3688\n",
      "we have 2 non-opposing shorted wedges for tract no 3689\n",
      "we have 2 non-opposing shorted wedges for tract no 3690\n",
      "I am working on tract number 3700 of 5411 tracts\n",
      "I am working on tract number 3720 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3720\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3737 7.0 0 88.1 1.9727 201238.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3737 8.0 0 88.1 1.9212 201238.1\n",
      "I am working on tract number 3740 of 5411 tracts\n",
      "I am working on tract number 3760 of 5411 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3776 3 266619.5807179958 1.1348\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3776 3 39068.6667533351 0.5674\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3776 3 265683.9522302634 1.102\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3776 3 101136.86410309054 0.8347\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3776 3 249266.8071944486 0.9683\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3776 3 212953.30020037963 0.9015\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3776 3 158109.3040499021 0.8681\n",
      "12 yoyos for tract,wedge,wedgePop,r= 3776 3 190132.97291396908 0.8848\n",
      "13 yoyos for tract,wedge,wedgePop,r= 3776 3 202464.0497630914 0.8931\n",
      "we have 2 non-opposing shorted wedges for tract no 3778\n",
      "we have 2 non-opposing shorted wedges for tract no 3779\n",
      "I am working on tract number 3780 of 5411 tracts\n",
      "I am working on tract number 3800 of 5411 tracts\n",
      "I am working on tract number 3820 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3834 9.0 0 84.0 1.0571 248759.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3834 10.0 0 84.0 0.8727 248759.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3836 3.0 3 90.0 1.0224 250608.8\n",
      "I am working on tract number 3840 of 5411 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3848 0 198775.94013712497 0.6768\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3848 0 13084.332443718595 0.3384\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3848 0 203248.9942731685 0.7095\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3848 0 71306.94473895855 0.5239\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3848 0 152723.1294663865 0.6167\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3851 3.0 0 90.0 1.0911 214590.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3851 4.0 0 90.0 1.0118 214590.0\n",
      "I am working on tract number 3860 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3871\n",
      "I am working on tract number 3880 of 5411 tracts\n",
      "I am working on tract number 3900 of 5411 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3904 1 331666.9450630947 1.9686\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3904 1 115194.20594866347 0.9843\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3904 1 331707.511904412 1.9688\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3904 1 245068.51867419606 1.4766\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3904 1 131169.656244655 1.2304\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3904 1 184785.68516726262 1.3535\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3904 1 223176.95273647882 1.415\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3904 1 211131.6530212954 1.3843\n",
      "I am working on tract number 3920 of 5411 tracts\n",
      "I am working on tract number 3940 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3944\n",
      "we have 2 non-opposing shorted wedges for tract no 3948\n",
      "I am working on tract number 3960 of 5411 tracts\n",
      "I am working on tract number 3980 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3998 3.0 3 40.0 0.9202 453878.5\n",
      "I am working on tract number 4000 of 5411 tracts\n",
      "I am working on tract number 4020 of 5411 tracts\n",
      "I am working on tract number 4040 of 5411 tracts\n",
      "I am working on tract number 4060 of 5411 tracts\n",
      "I am working on tract number 4080 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4082 2.0 0 90.0 0.4089 207227.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4082 3.0 0 90.0 0.3894 207227.0\n",
      "we have 2 non-opposing shorted wedges for tract no 4095\n",
      "I am working on tract number 4100 of 5411 tracts\n",
      "I am working on tract number 4120 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4130\n",
      "we have 2 non-opposing shorted wedges for tract no 4138\n",
      "I am working on tract number 4140 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4147 6.0 1 45.7 2.396 258926.4\n",
      "I am working on tract number 4160 of 5411 tracts\n",
      "I am working on tract number 4180 of 5411 tracts\n",
      "I am working on tract number 4200 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4212 5.0 3 36.8 2.0705 547171.7\n",
      "I am working on tract number 4220 of 5411 tracts\n",
      "I am working on tract number 4240 of 5411 tracts\n",
      "I am working on tract number 4260 of 5411 tracts\n",
      "I am working on tract number 4280 of 5411 tracts\n",
      "I am working on tract number 4300 of 5411 tracts\n",
      "I am working on tract number 4320 of 5411 tracts\n",
      "I am working on tract number 4340 of 5411 tracts\n",
      "I am working on tract number 4360 of 5411 tracts\n",
      "I am working on tract number 4380 of 5411 tracts\n",
      "I am working on tract number 4400 of 5411 tracts\n",
      "I am working on tract number 4420 of 5411 tracts\n",
      "I am working on tract number 4440 of 5411 tracts\n",
      "I am working on tract number 4460 of 5411 tracts\n",
      "I am working on tract number 4480 of 5411 tracts\n",
      "I am working on tract number 4500 of 5411 tracts\n",
      "I am working on tract number 4520 of 5411 tracts\n",
      "I am working on tract number 4540 of 5411 tracts\n",
      "I am working on tract number 4560 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4570\n",
      "we have 2 non-opposing shorted wedges for tract no 4572\n",
      "we have 2 non-opposing shorted wedges for tract no 4573\n",
      "we have 2 non-opposing shorted wedges for tract no 4574\n",
      "I am working on tract number 4580 of 5411 tracts\n",
      "I am working on tract number 4600 of 5411 tracts\n",
      "I am working on tract number 4620 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4635\n",
      "I am working on tract number 4640 of 5411 tracts\n",
      "I am working on tract number 4660 of 5411 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4672 3 646142.9459603298 1.3516\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4672 3 59899.49883874389 0.6758\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4672 3 749264.5103256286 1.4489\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4672 3 132874.90800680115 1.0623\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4672 3 467892.5974717447 1.2556\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4672 3 221437.7073488377 1.159\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4672 3 314377.5222811286 1.2073\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4672 3 392263.9469847365 1.2314\n",
      "13 yoyos for tract,wedge,wedgePop,r= 4672 3 355765.76213608833 1.2194\n",
      "14 yoyos for tract,wedge,wedgePop,r= 4672 3 335149.9067628978 1.2133\n",
      "I am working on tract number 4680 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4694\n",
      "I am working on tract number 4700 of 5411 tracts\n",
      "I am working on tract number 4720 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4732\n",
      "I am working on tract number 4740 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4754\n",
      "we have 2 non-opposing shorted wedges for tract no 4755\n",
      "I am working on tract number 4760 of 5411 tracts\n",
      "I am working on tract number 4780 of 5411 tracts\n",
      "I am working on tract number 4800 of 5411 tracts\n",
      "I am working on tract number 4820 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4824 3.0 3 36.2 1.0556 332202.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4832 3.0 0 88.9 0.6112 198514.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4832 4.0 0 88.9 0.6013 198514.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4832 5.0 0 88.9 0.5917 198514.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4832 6.0 0 88.9 0.5821 198514.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4839 0 243411.27078785497 0.6356\n",
      "I am working on tract number 4840 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4852 3.0 1 43.7 0.9133 404054.3\n",
      "I am working on tract number 4860 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4864\n",
      "we have 2 non-opposing shorted wedges for tract no 4867\n",
      "I am working on tract number 4880 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4894\n",
      "I am working on tract number 4900 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4912\n",
      "we have 2 non-opposing shorted wedges for tract no 4916\n",
      "I am working on tract number 4920 of 5411 tracts\n",
      "I am working on tract number 4940 of 5411 tracts\n",
      "I am working on tract number 4960 of 5411 tracts\n",
      "I am working on tract number 4980 of 5411 tracts\n",
      "I am working on tract number 5000 of 5411 tracts\n",
      "I am working on tract number 5020 of 5411 tracts\n",
      "I am working on tract number 5040 of 5411 tracts\n",
      "I am working on tract number 5060 of 5411 tracts\n",
      "I am working on tract number 5080 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 5087\n",
      "I am working on tract number 5100 of 5411 tracts\n",
      "I am working on tract number 5120 of 5411 tracts\n",
      "I am working on tract number 5140 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5140 3.0 0 87.4 0.6164 201779.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5140 4.0 0 87.4 0.5991 201779.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5141 3.0 1 36.3 0.6566 347976.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5152 7.0 0 116.0 0.4923 230647.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5152 8.0 0 116.0 0.4316 230647.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5153 7.0 0 117.3 0.4907 235233.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5153 8.0 0 117.3 0.4236 235233.2\n",
      "I am working on tract number 5160 of 5411 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5163 7.0 0 113.4 0.408 230438.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5164 4.0 0 90.0 0.444 201982.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5164 5.0 0 90.0 0.4312 201982.1\n",
      "we have 2 non-opposing shorted wedges for tract no 5165\n",
      "we have 2 non-opposing shorted wedges for tract no 5166\n",
      "we have 2 non-opposing shorted wedges for tract no 5167\n",
      "we have 2 non-opposing shorted wedges for tract no 5169\n",
      "I am working on tract number 5180 of 5411 tracts\n",
      "I am working on tract number 5200 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 5206\n",
      "I am working on tract number 5220 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 5235\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5236 3 1015219.5903176959 1.1314\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5236 3 47569.989012795966 0.5657\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5236 3 1017049.6538797858 1.1417\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5236 3 790639.7997678975 0.8537\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5236 3 266872.34907363664 0.7097\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5236 3 598423.8659937237 0.7817\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5236 3 434066.15090871975 0.7457\n",
      "12 yoyos for tract,wedge,wedgePop,r= 5236 3 346549.9883767711 0.7277\n",
      "13 yoyos for tract,wedge,wedgePop,r= 5236 3 391109.71904520976 0.7367\n",
      "we have 2 non-opposing shorted wedges for tract no 5239\n",
      "I am working on tract number 5240 of 5411 tracts\n",
      "I am working on tract number 5260 of 5411 tracts\n",
      "I am working on tract number 5280 of 5411 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 5293\n",
      "I am working on tract number 5300 of 5411 tracts\n",
      "I am working on tract number 5320 of 5411 tracts\n",
      "I am working on tract number 5340 of 5411 tracts\n",
      "I am working on tract number 5360 of 5411 tracts\n",
      "I am working on tract number 5380 of 5411 tracts\n",
      "I am working on tract number 5400 of 5411 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.003188327170673\n",
      "Average and max number of wedgePop loops per tract were:  3.2481981149510255 28.0\n",
      "min,average,max of Home District areas were:  0.0 0.6072145299513547 5.048784619955022\n",
      "calculated statewide vote was 0.47992261710094647, should have been 0.47692503683203646\n",
      "calcd statewide Hispanic pop was 0.07017789171693564, should have been 0.028221854180114723\n",
      "calcd statewide Black pop was 0.07122943950168296, should have been 0.030313954973649835\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.2315653298835705\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "#  and 3/23/22 min of 5pct wedge on map to continue growth\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    if isEmptyGrid[nG] == 0: #NEW 3/18/22 TO SAVE A LITTLE TIME, skip empty grids\n",
    "                        gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                        if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                            for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                                nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                                if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                    usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                    overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                    if overlap > 0 :\n",
    "                                        fracArea = overlap/tractArea[nTT]\n",
    "                                        latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                        loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                            # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])  #leading edge of growing wedgePoly\n",
    "                    fracArea = wedgePoly.intersection(MAP).area / max(0.00000001,wedgePoly.area) #what pct of new wedge piece on map?\n",
    "                    if leadingEdge.intersects(MAP) and fracArea > 0.05 :  #still room to grow in part of edge...\n",
    "                        conclusion = \"keep going\"  #** NEW 3/23/22 - add fracArea criterion on top of leadingEdge criterion *****\n",
    "                    else: #conclude this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "\n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.003188327170673\n",
      "Average and max number of wedgePop loops per tract were:  3.2481981149510255 28.0\n",
      "min,average,max of Home District areas were:  0.0 0.6072145299513547 5.048784619955022 for  NYup\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start NY upstate 12:04pm, end 1:17pm\n",
    "STATE = \"NYup\"\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NYup 27Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"27Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"tractVAP\":tractVAP,\"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "24e821dc-ad97-4a68-95aa-c05296c429c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "wholeMAP = tractMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "603 0.652649436403434 792 pop,GOP 0.7487373737373737\n",
      "607 0.6170702201480671 649 pop,GOP 0.8120184899845917\n",
      "608 0.7414301596798797 797 pop,GOP 0.6398996235884568\n",
      "614 0.7821671664445798 624 pop,GOP 0.780448717948718\n",
      "615 0.741290378145568 608 pop,GOP 0.5986842105263158\n",
      "617 0.6462552771556996 709 pop,GOP 0.5401974612129761\n",
      "618 0.5887979311718933 459 pop,GOP 0.8126361655773421\n",
      "619 0.5955295193366957 533 pop,GOP 0.701688555347092\n",
      "620 0.6095861770411037 632 pop,GOP 0.7373417721518988\n",
      "621 0.7635668288665128 438 pop,GOP 0.5799086757990868\n",
      "625 0.6361070220334172 234 pop,GOP 0.6837606837606838\n",
      "626 0.7516577325558871 787 pop,GOP 0.542566709021601\n",
      "627 0.7510255193690387 418 pop,GOP 0.4569377990430622\n",
      "628 0.7916365485295369 706 pop,GOP 0.5708215297450425\n",
      "630 0.704928121679952 2913 pop,GOP 0.5849639546858908\n",
      "631 0.6773467010597065 1331 pop,GOP 0.7084898572501879\n",
      "632 0.7984743566038247 1780 pop,GOP 0.7477528089887641\n",
      "633 0.6548132516052839 1403 pop,GOP 0.6058446186742694\n",
      "638 0.7814805789512405 428 pop,GOP 0.6658878504672897\n",
      "643 0.7867126311515475 684 pop,GOP 0.5029239766081871\n",
      "645 0.7700331721533614 1593 pop,GOP 0.5141242937853108\n",
      "646 0.6489388977460675 1282 pop,GOP 0.6396255850234009\n",
      "650 0.7169720451268149 1143 pop,GOP 0.6552930883639545\n",
      "651 0.6004811660587587 1071 pop,GOP 0.6956115779645191\n",
      "653 0.6271742531175717 2439 pop,GOP 0.6072160721607216\n",
      "654 0.7448822898832292 1416 pop,GOP 0.5572033898305084\n",
      "655 0.742252373157279 2242 pop,GOP 0.644513826940232\n",
      "656 0.6812246994637796 204 pop,GOP 0.5588235294117647\n",
      "658 0.7925851246647933 1212 pop,GOP 0.5181518151815182\n",
      "659 0.7801704371166878 1235 pop,GOP 0.540080971659919\n",
      "660 0.7563019766440136 1451 pop,GOP 0.5403170227429359\n",
      "782 0.7590849670509987 59 pop,GOP 0.576271186440678\n",
      "783 0.7528223566603514 371 pop,GOP 0.261455525606469\n",
      "784 0.7406239633478695 499 pop,GOP 0.2685370741482966\n",
      "785 0.772429578784498 735 pop,GOP 0.3129251700680272\n",
      "786 0.7714907328181113 275 pop,GOP 0.2909090909090909\n",
      "787 0.7437743732978167 881 pop,GOP 0.3098751418842225\n",
      "788 0.7548789402342178 751 pop,GOP 0.3209054593874834\n",
      "789 0.7830497348111108 563 pop,GOP 0.35168738898756663\n",
      "790 0.790363039098236 188 pop,GOP 0.5691489361702128\n",
      "791 0.792487302262915 334 pop,GOP 0.39221556886227543\n",
      "792 0.7550416409071765 630 pop,GOP 0.2507936507936508\n",
      "793 0.7923703489196371 659 pop,GOP 0.3186646433990895\n",
      "794 0.7275371760599804 841 pop,GOP 0.2877526753864447\n",
      "795 0.7776935094330961 1179 pop,GOP 0.32061068702290074\n",
      "797 0.7571873184378404 910 pop,GOP 0.46813186813186813\n",
      "798 0.7651189571122788 520 pop,GOP 0.39807692307692305\n",
      "800 0.7267512666781015 726 pop,GOP 0.5495867768595041\n",
      "801 0.7005807389887693 567 pop,GOP 0.4656084656084656\n",
      "802 0.7084398078030939 1246 pop,GOP 0.4028892455858748\n",
      "803 0.6979599732183346 605 pop,GOP 0.509090909090909\n",
      "805 0.6332460986160122 858 pop,GOP 0.4731934731934732\n",
      "808 0.7793220500992347 846 pop,GOP 0.466903073286052\n",
      "810 0.7962787680285324 778 pop,GOP 0.39460154241645246\n",
      "811 0.7246691215883694 775 pop,GOP 0.5367741935483871\n",
      "812 0.6403566006974326 826 pop,GOP 0.5012106537530266\n",
      "814 0.6633086009398018 1007 pop,GOP 0.45779543197616684\n",
      "815 0.7641923757503687 332 pop,GOP 0.5783132530120482\n",
      "816 0.6843357893840378 809 pop,GOP 0.4227441285537701\n",
      "819 0.7987907012427036 686 pop,GOP 0.48833819241982507\n",
      "820 0.6450922743412572 882 pop,GOP 0.5215419501133787\n",
      "821 0.6473769733165131 1027 pop,GOP 0.6037000973709834\n",
      "823 0.6704360774428035 546 pop,GOP 0.6355311355311355\n",
      "824 0.6744257019278184 948 pop,GOP 0.4989451476793249\n",
      "826 0.6901129863231736 304 pop,GOP 0.6217105263157895\n",
      "827 0.6890237750153902 1015 pop,GOP 0.6068965517241379\n",
      "831 0.7769979935922472 342 pop,GOP 0.5760233918128655\n",
      "832 0.7123257789663654 871 pop,GOP 0.6234213547646383\n",
      "1223 0.7903687335084052 766 pop,GOP 0.5091383812010444\n",
      "1226 0.7966252913270101 345 pop,GOP 0.3565217391304348\n",
      "1227 0.78991176639962 272 pop,GOP 0.4522058823529412\n",
      "1385 0.7964807108252374 777 pop,GOP 0.5096525096525096\n",
      "1387 0.7872938272934944 606 pop,GOP 0.5561056105610561\n",
      "2095 0.7747932113683438 249 pop,GOP 0.7188755020080321\n",
      "2097 0.6883037728764799 601 pop,GOP 0.6605657237936772\n",
      "2098 0.6922363936497549 374 pop,GOP 0.5588235294117647\n",
      "2099 0.6856225114340816 401 pop,GOP 0.5461346633416458\n",
      "2100 0.7947891418656609 413 pop,GOP 0.7312348668280871\n",
      "2102 0.6571150714296177 464 pop,GOP 0.46336206896551724\n",
      "2103 0.7453573157572568 561 pop,GOP 0.6131907308377896\n",
      "2104 0.7175262097623909 279 pop,GOP 0.5770609318996416\n",
      "2105 0.7886767201521642 532 pop,GOP 0.5413533834586466\n",
      "2106 0.7020580614440713 275 pop,GOP 0.41818181818181815\n",
      "2107 0.7180313262855342 237 pop,GOP 0.41350210970464135\n",
      "2108 0.7124584681394996 472 pop,GOP 0.4004237288135593\n",
      "2109 0.689773863449001 581 pop,GOP 0.6041308089500861\n",
      "2110 0.7195949789423907 607 pop,GOP 0.6935749588138386\n",
      "2113 0.6719913645386042 357 pop,GOP 0.6554621848739496\n",
      "2114 0.6681930403981836 320 pop,GOP 0.653125\n",
      "2115 0.6784704281418336 137 pop,GOP 0.5474452554744526\n",
      "2116 0.6737877115252335 401 pop,GOP 0.5411471321695761\n",
      "2117 0.636880247170513 688 pop,GOP 0.06686046511627906\n",
      "2118 0.6780704531141208 526 pop,GOP 0.629277566539924\n",
      "2119 0.6625404151077972 116 pop,GOP 0.7586206896551724\n",
      "2120 0.7170967796798835 372 pop,GOP 0.46236559139784944\n",
      "2121 0.7096524552423982 368 pop,GOP 0.43478260869565216\n",
      "2123 0.7302406019380712 494 pop,GOP 0.5668016194331984\n",
      "2132 0.7522984567984637 303 pop,GOP 0.6204620462046204\n",
      "2133 0.7335740374573516 474 pop,GOP 0.6392405063291139\n",
      "2134 0.7107891097687103 500 pop,GOP 0.624\n",
      "2135 0.6959147950050651 462 pop,GOP 0.6688311688311688\n",
      "2595 0.7619549135546767 523 pop,GOP 0.42829827915869984\n",
      "2596 0.7704605918014891 161 pop,GOP 0.4472049689440994\n",
      "2600 0.7586921449974681 622 pop,GOP 0.3762057877813505\n",
      "2602 0.793893831636119 427 pop,GOP 0.40046838407494145\n",
      "2611 0.7974486577050455 468 pop,GOP 0.4358974358974359\n",
      "2613 0.7787885758250567 384 pop,GOP 0.4114583333333333\n",
      "2615 0.7810025595791841 362 pop,GOP 0.3701657458563536\n",
      "2616 0.7821063341635892 744 pop,GOP 0.34543010752688175\n",
      "2653 0.7937119187337672 555 pop,GOP 0.4972972972972973\n",
      "2654 0.760939122091338 439 pop,GOP 0.44646924829157175\n",
      "2655 0.7820279659344169 535 pop,GOP 0.4654205607476635\n",
      "2678 0.7959740232721628 540 pop,GOP 0.3333333333333333\n",
      "2687 0.7697837856908532 472 pop,GOP 0.4343220338983051\n",
      "2688 0.7894967601770411 610 pop,GOP 0.5049180327868853\n",
      "2691 0.7812921224324462 334 pop,GOP 0.4281437125748503\n",
      "2697 0.7945056894020678 365 pop,GOP 0.4465753424657534\n",
      "2705 0.7982126677793285 525 pop,GOP 0.4704761904761905\n",
      "2711 0.7287042052546341 321 pop,GOP 0.49221183800623053\n",
      "2713 0.7920467026658088 471 pop,GOP 0.43736730360934184\n",
      "2714 0.7291336509463494 609 pop,GOP 0.5188834154351396\n",
      "2717 0.7816086502591139 713 pop,GOP 0.5273492286115007\n",
      "2718 0.7471263577087013 643 pop,GOP 0.5225505443234837\n",
      "2719 0.7349707839451025 945 pop,GOP 0.4433862433862434\n",
      "2722 0.7968286626222739 624 pop,GOP 0.4342948717948718\n",
      "2726 0.7599465452684415 732 pop,GOP 0.46584699453551914\n",
      "2727 0.7264678130513571 568 pop,GOP 0.5563380281690141\n",
      "2728 0.7500977109378939 582 pop,GOP 0.36254295532646047\n",
      "2734 0.750618512624641 247 pop,GOP 0.3562753036437247\n",
      "2735 0.781761246815172 532 pop,GOP 0.4642857142857143\n",
      "2736 0.7723600781335587 669 pop,GOP 0.4947683109118087\n",
      "2741 0.7433450015165924 416 pop,GOP 0.5096153846153846\n",
      "2742 0.7392951807832255 615 pop,GOP 0.44390243902439025\n",
      "2745 0.745530045722466 484 pop,GOP 0.39462809917355374\n",
      "2746 0.7832891173233857 623 pop,GOP 0.3113964686998395\n",
      "2783 0.7688583706701241 694 pop,GOP 0.3472622478386167\n",
      "2785 0.7790916299483828 381 pop,GOP 0.4881889763779528\n",
      "2788 0.7626887918624253 404 pop,GOP 0.43564356435643564\n",
      "2795 0.7646031243904725 466 pop,GOP 0.37124463519313305\n",
      "2796 0.7579845024742707 515 pop,GOP 0.4912621359223301\n",
      "2797 0.784627429317664 793 pop,GOP 0.47414880201765447\n",
      "2800 0.7636264638594733 392 pop,GOP 0.4744897959183674\n",
      "2804 0.7847021052745649 468 pop,GOP 0.3333333333333333\n",
      "2805 0.7713759761986148 450 pop,GOP 0.39111111111111113\n",
      "2818 0.7239313040405522 573 pop,GOP 0.4397905759162304\n",
      "2819 0.7606745182379318 435 pop,GOP 0.3471264367816092\n",
      "2831 0.7425396467147454 417 pop,GOP 0.4196642685851319\n",
      "2854 0.7075326043065369 675 pop,GOP 0.5466666666666666\n",
      "2855 0.7842361896129613 448 pop,GOP 0.45982142857142855\n",
      "2856 0.7471048911832041 276 pop,GOP 0.38405797101449274\n",
      "2900 0.7550262536489795 412 pop,GOP 0.4077669902912621\n",
      "2901 0.7496182616031565 416 pop,GOP 0.36778846153846156\n",
      "2909 0.7064796994837227 384 pop,GOP 0.4739583333333333\n",
      "2928 0.7329768050732605 14 pop,GOP 0.07142857142857142\n",
      "2958 0.7822003997504552 315 pop,GOP 0.4634920634920635\n",
      "2982 0.7885754233841017 318 pop,GOP 0.4088050314465409\n",
      "2983 0.7708219227537308 383 pop,GOP 0.39947780678851175\n",
      "2984 0.7454058456245669 359 pop,GOP 0.403899721448468\n",
      "3013 0.7774133794517902 431 pop,GOP 0.4431554524361949\n",
      "3014 0.7627527552851743 512 pop,GOP 0.431640625\n",
      "3017 0.7402526572751587 336 pop,GOP 0.37202380952380953\n",
      "3018 0.7422484019421525 408 pop,GOP 0.3897058823529412\n",
      "3019 0.7294755179079697 481 pop,GOP 0.49480249480249483\n",
      "3036 0.7472683504875153 801 pop,GOP 0.5243445692883895\n",
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      "3054 0.7603254453302564 235 pop,GOP 0.4595744680851064\n",
      "3061 0.7795824232871799 561 pop,GOP 0.43493761140819964\n",
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      "3103 0.7188174256882487 490 pop,GOP 0.49795918367346936\n",
      "3151 0.7510893228659502 463 pop,GOP 0.42332613390928725\n",
      "4700 0.714140253677804 471 pop,GOP 0.4437367303609342\n",
      "4711 0.7919810456298515 440 pop,GOP 0.31363636363636366\n",
      "4715 0.7977585154375412 508 pop,GOP 0.4389763779527559\n",
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      "4726 0.7581394011319879 271 pop,GOP 0.23247232472324722\n",
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      "12167 0.7467434003265253 512 pop,GOP 0.470703125\n",
      "12168 0.6587112452608945 560 pop,GOP 0.6160714285714286\n",
      "12169 0.6845481217014713 901 pop,GOP 0.537180910099889\n",
      "12170 0.7392352115257592 393 pop,GOP 0.4961832061068702\n",
      "12171 0.6615047893630417 636 pop,GOP 0.589622641509434\n",
      "12172 0.74653341933734 43 pop,GOP 0.4883720930232558\n",
      "12173 0.727892374880018 661 pop,GOP 0.4886535552193646\n",
      "12174 0.13231083379966613 967 pop,GOP 0.45708376421923474\n",
      "12175 0.7166288790249088 815 pop,GOP 0.47975460122699387\n",
      "12176 0.7150169218194221 926 pop,GOP 0.48596112311015116\n",
      "12177 0.6990882788164398 408 pop,GOP 0.4632352941176471\n",
      "12180 0.7013377143407972 487 pop,GOP 0.3798767967145791\n",
      "12257 0.790392887980038 449 pop,GOP 0.5612472160356348\n",
      "12259 0.7929693658953004 271 pop,GOP 0.3210332103321033\n",
      "12260 0.772135809490825 631 pop,GOP 0.5816164817749604\n",
      "12261 0.755032333366962 673 pop,GOP 0.6731054977711739\n",
      "12262 0.7610602531090842 414 pop,GOP 0.5821256038647343\n",
      "12263 0.7356795662808651 780 pop,GOP 0.4807692307692308\n",
      "12264 0.748115990216374 343 pop,GOP 0.5451895043731778\n",
      "12265 0.7607879600785988 601 pop,GOP 0.5141430948419301\n",
      "12266 0.709740161257694 720 pop,GOP 0.4875\n",
      "12267 0.7125598554391349 410 pop,GOP 0.4951219512195122\n",
      "12268 0.684351228432227 815 pop,GOP 0.48711656441717793\n",
      "12294 0.7676900022832049 601 pop,GOP 0.33943427620632277\n",
      "12310 0.7855334472012537 884 pop,GOP 0.24547511312217193\n",
      "12763 0.6720021234717041 433 pop,GOP 0.6836027713625866\n",
      "12764 0.7145771730369384 582 pop,GOP 0.5876288659793815\n",
      "12765 0.7652246577243327 671 pop,GOP 0.6318926974664679\n",
      "12766 0.6771329859844863 863 pop,GOP 0.4461181923522596\n",
      "12767 0.6674902700618178 605 pop,GOP 0.44958677685950416\n",
      "12768 0.6584466182174439 675 pop,GOP 0.5525925925925926\n",
      "12769 0.6572951512481033 608 pop,GOP 0.4786184210526316\n",
      "12770 0.6513986350632606 559 pop,GOP 0.5080500894454383\n",
      "12771 0.6619067003692272 509 pop,GOP 0.6247544204322201\n",
      "12772 0.6848249569675284 499 pop,GOP 0.5470941883767535\n",
      "12773 0.6861526808534191 370 pop,GOP 0.572972972972973\n",
      "12774 0.6796819912096176 275 pop,GOP 0.49454545454545457\n",
      "12775 0.7280889737872058 573 pop,GOP 0.6143106457242583\n",
      "12776 0.7443760638737894 597 pop,GOP 0.5745393634840871\n",
      "12778 0.751322602055585 591 pop,GOP 0.6023688663282571\n",
      "12779 0.7949863449875799 333 pop,GOP 0.6726726726726727\n",
      "12780 0.7519172237035644 747 pop,GOP 0.5475234270414994\n",
      "12785 0.6907907312391739 580 pop,GOP 0.5206896551724138\n",
      "12786 0.7048010306957413 705 pop,GOP 0.6382978723404256\n",
      "12791 0.719755469634186 700 pop,GOP 0.6542857142857142\n",
      "12792 0.789325696831969 552 pop,GOP 0.697463768115942\n",
      "12793 0.7647335845913906 618 pop,GOP 0.7168284789644013\n",
      "12797 0.752277235212295 563 pop,GOP 0.6127886323268206\n",
      "12798 0.7593004277089622 576 pop,GOP 0.6493055555555556\n",
      "12799 0.7566625947944522 712 pop,GOP 0.5617977528089888\n",
      "12800 0.7637976480978572 673 pop,GOP 0.5928677563150074\n",
      "12801 0.7509296425668588 604 pop,GOP 0.6043046357615894\n",
      "12802 0.7494515745232788 625 pop,GOP 0.5472\n",
      "12804 0.7764330528797359 700 pop,GOP 0.6485714285714286\n",
      "12810 0.7683919328813558 1508 pop,GOP 0.6226790450928382\n",
      "12812 0.7806644516161123 356 pop,GOP 0.702247191011236\n",
      "12813 0.7890247968459783 585 pop,GOP 0.6632478632478632\n",
      "12832 0.660611200274203 704 pop,GOP 0.5284090909090909\n",
      "12833 0.6290127111930433 476 pop,GOP 0.5651260504201681\n",
      "12834 0.7137086749802553 670 pop,GOP 0.5925373134328358\n",
      "12835 0.6724087088774479 336 pop,GOP 0.49404761904761907\n",
      "12836 0.7778045327142132 529 pop,GOP 0.553875236294896\n",
      "12837 0.7588909784288722 726 pop,GOP 0.4393939393939394\n",
      "14498 0.7251206821192214 490 pop,GOP 0.2714285714285714\n",
      "14499 0.7904635796452788 587 pop,GOP 0.262350936967632\n",
      "14508 0.6860489698324631 600 pop,GOP 0.315\n",
      "14509 0.702463783461887 355 pop,GOP 0.2704225352112676\n",
      "14510 0.6731021912955338 718 pop,GOP 0.3579387186629526\n",
      "14511 0.6999785833319055 449 pop,GOP 0.32962138084632514\n",
      "14521 0.7040177515680883 460 pop,GOP 0.3239130434782609\n",
      "14522 0.7074930002703836 623 pop,GOP 0.33226324237560195\n",
      "14523 0.7593372105914594 424 pop,GOP 0.30660377358490565\n",
      "14524 0.7615833148025808 617 pop,GOP 0.29983792544570503\n",
      "14525 0.7647157121381175 325 pop,GOP 0.2953846153846154\n",
      "14526 0.7712145707529122 778 pop,GOP 0.23136246786632392\n",
      "14532 0.7820457353377128 800 pop,GOP 0.32875\n",
      "14540 0.6900425896762408 769 pop,GOP 0.3081924577373212\n",
      "14559 0.7910485648619088 649 pop,GOP 0.12480739599383667\n",
      "14571 0.7938452810033585 348 pop,GOP 0.1896551724137931\n",
      "14572 0.7586891672362208 554 pop,GOP 0.2996389891696751\n",
      "14573 0.47935292637079235 594 pop,GOP 0.37373737373737376\n",
      "14616 0.7468113723848296 799 pop,GOP 0.2640801001251564\n",
      "14617 0.7656055623695439 1004 pop,GOP 0.3545816733067729\n",
      "14618 0.7893378283154094 639 pop,GOP 0.3380281690140845\n",
      "14619 0.7892931555133671 716 pop,GOP 0.2541899441340782\n",
      "14654 0.0029840258041431375 839 pop,GOP 0.2908224076281287\n",
      "14662 0.7352471822240615 634 pop,GOP 0.30126182965299686\n",
      "14663 0.7363670018049815 463 pop,GOP 0.30885529157667385\n",
      "14664 0.7900239803218008 589 pop,GOP 0.29711375212224106\n",
      "14666 0.7580629198364209 590 pop,GOP 0.33728813559322035\n",
      "14667 0.7703444411480651 607 pop,GOP 0.3014827018121911\n",
      "14670 0.7518832061186745 740 pop,GOP 0.49864864864864866\n",
      "14671 0.7692598601857535 571 pop,GOP 0.3222416812609457\n",
      "14672 0.7256457471021603 843 pop,GOP 0.4092526690391459\n",
      "14673 0.7002798356749934 476 pop,GOP 0.39705882352941174\n",
      "14674 0.6998733037740473 522 pop,GOP 0.342911877394636\n",
      "14675 0.6964286954354652 879 pop,GOP 0.2241183162684869\n",
      "15235 0.7901100478176871 379 pop,GOP 0.39841688654353563\n",
      "15237 0.7832804167747152 650 pop,GOP 0.32\n",
      "15286 0.7528693241623724 943 pop,GOP 0.26935312831389185\n",
      "15288 0.7889951617659774 898 pop,GOP 0.30734966592427615\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        plotPoly(vtdGeom[p])\n",
    "\n",
    "plotPoly(wholeMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in NYup\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 2 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13525927100234525 = NYup std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1353580402791836 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for NYup\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  7.9136\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.003 0.47992 HD avg vs true 0.47693\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.07018, should have been 0.07367\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for NYC-LI\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 3.089\n"
     ]
    },
    {
     "data": {
      "image/png": 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XjZIoQDcrc2RSZJmvVUDhahadgEFCiP6ADeAghFglpSzMiLEwoGau155AeNHDVIQQTO08lQbODRiyfojJ1CoAPBw8MBNmBN0J4tkmzxo7HKUC0UotV+9fxT/Mnw0XNrDjyg40Wg3NXJux+8XdRksSoKtVzD08l561e9LZq7PR4tCXApOFlHIaMA1ACNEdmFzIRAFwAvARQtQGbgMjgeLNiKcAuiVVAVzsTKf2ZWdpRyWrSkQnRxs7FKWcSc9MJzwhnLD4MG7H3yYsPkz3PEH3/HzUeeLT4gFwr+zOhHYTGNVsFC1qtDD6WIbVZ1ZzN+ku3/p+a9Q49KXYSzUJIYYA3wAuwDYhRJCU8ikhhDuwVErZX0qZIYR4G9gJmAPLpZTn9RJ5BXUv+R6ASdUs0jLSSNGk6G35SUU5eusoHx/4mJ1XdiIfarm2t7TH08ETDwcPXmj6Ar7uvrR2b00z12alMmtsYfm6++Jo48iEvyZQp2qdnGUHyiphinfX+Pr6yoCAAGOHYZIm7ZjE0lNLSfwg0dihPOCVza+w5uwazrx+hgbOxVyvVXlApjaTS9GXsDK3op5TPWOHUyoOhR7io/0fsfvabpztnHm15avUr1Yfj8oeOQmiinUVo9caCut85Hn6r+nPveR7rBm6hsENDXczqBDipJTSYLckqmRRxjy16imikqI4Nf4UwcEwZgycPKlb3EgI8PSEb7+FQYNKN647iXdotKgRT1R/gn/G/FOq97GXB5pMDReiLnAq4hSnIk5xMuIkp++eJlmTDEBD54Y0dG5IqxqtaOXWitburalRqYaRo37Q3cS7bA3Zyt4be6lmW40mLk1o6tqUJq5NcLRxzPe4TG0m+2/uZ9aBWfxz4x9c7V15r+N7vOH7BvZW9qX3CxjIncQ7DFo7iIDwAL7o8wXvtn/XIMlOJQvlAR7zPXiyzpMsHbCSRo3g/n1dkhg0CNatAynB3BxOnYIGpfwFf9mpZby25TV+GPgDfq39SvfkZdTfV/9m+t7pnLl7hrTMNAAqW1WmpVtLWtXQJYV7yffYc30Pl6MvExIdktMs41bJjdburWnt1jpn9HyGNoMMbQaZ2syc5xnaDDLlv6/TMtKITIrkbtJdqthUoV7VetRz0v00dG5IZevC3SGfrEnmcvRltl3expaQLRwLO4ZEUqNSDRLTE0lM/7f2617Z/d/k4dIETwdPTkac5GDoQQ6HHiYhPYEalWowpeMUxvuOL/KKkKYuWZPMS5te4vfg3xneeDg/Df6JSlaV9HoOlSyUHPdT7lNtbjXmPjmX9tr3eOYZXY3C2hr27IEePSA6GiwtdWtWrF1buvFJKen5c08CIwIJfisYt8pupRtAGfTervf44ugX/Kf9f3La3us51cu3ZpaQlkDQnSBORpzMqYFcvHcRrdTmuX9eBAJnO2dc7V2JTY3ldsKDC7LXqlKLpq5NaebajAbODUhKT+J2wm3CE8L/fYy/TVxaXM4xvu6+DKo/iKcbPE3z6s2RSG7F3eJc5DnOR53X/USe50LUBVIyUnKOa+LShC5eXehaqyvPNHwGW0vbIn6CZYdWahm9cTRrz63l7xf/5sk6T+q1fEMni2J3cCul7+zdswA0q96M8JPg6Ajp6RAbC088ATY2umYoCwv4/Xe4dKl0axdCCJYMXEKzxc145693+O3Z30rv5GVUdl/ExPYT8apS8GDUytaV6VKrC11qdcnZlpSeRGhcKOZm5liYWWAudI/ZP9nbs98zNzN/IBkla5K5HnOdy/cvcyHqAuciz3Eu8hy7ru5Co9UAYC7McavshkdlDxpUa0BP7554OHhQ06EmPWr3wL2y+wNxCgS1HGtRy7EWA+oPyNmeqc3kRuwNQuNCaVa9mUndqGFoZsKMD7p8wNpza7l472JOspBSlo0+GCmlyf20bt1aKo/65tg3kpnIsLgweeCAlFWrSmlmJqWNjZSurlL26KF77eMjZdOmUn74oXHi/PTAp5KZyE3Bm4wTgJRSq9XKu4l3ZUxKjEzRpEitVmu0WB5nz7U9kpnI3Vd3GzuUR6RnpMuQeyEyIiFCZmozjR1OudHi+xayxfctpFarle/tek8yE3np3qUSlwsESAP+XVa9kGXI2btnqWpTFffK7rRrp6tJSAlmZrrmqKNHdftdvw7t28Pt248vz1Amd5zME9Wf4K3tbxGXGlfwAQYw68Asqn9RnaqfV8V2ti1mH5thM8uGVj+0yrn92BRk9zVcvn/ZyJE8ytLcEp9qPtSoVEPdsKBHfq38CLoTRNcVXZl3ZB4AQXeCjBtUIagroAw5F3WOZtWbIYTg6lVdgpASkpPh3j1ITQUrK+jeHZKSwMPDOHFamlvy49M/EpEQwbQ904wTRJZZPWYxp9ccPuz2IW+3fZsLURcYtXEUmdpMo8aVzb2yO7YWtlyONr1koRjG6CdGU8mqEv5h/szuORuPyh58tP8jNJkaY4f2WKrPooyQUnIu8hyjm41Go4GBAyEyUvees7MuWQCkpUGlSrBlC5w4Ybx423q0ZWK7iSw4toBRzUbpfaGZZE0y1ubW+Q7CaujcENCt4teiRouc7fWr1Wf81vHMPjib/3X7n15jKg4zYUY9p3qE3A8xdihKKalsXZkdo3Zgb2VPixot8HHy4dnfnuWPi38woskIY4eXL1WzKCNC40KJT4unWfVm+PvDnTu6WgXobp/NJiXs3Ak//wwNGxon1myf9PyEWlVqMW7LONIy0vRSpiZTwyf7P6HKZ1VouKghy04tIz0z/ZH9sgcGXrr34Cz641qN48UnXmTmvpkcvHlQLzGVlE81H1WzqGA6eXXK+RLj6eAJYPIzIKhkUUacizwHQDPXZoSG6moQZln/etqH7ppMS4PGJrDEVCWrSiwesJjge8F85f9VicsLiQ6h4/KO/G/f/3i6/tNUtqrMa1teo+7Cunzt/zVJ6Uk5+/o4+SAQXLx38YEyhBAsHrCYmlVqMnHHxCLdcmooPk4+XIu5RoY2w9ihKEbgH+YPYPILI6lkUUZcitZ9Q27k0oiYGF2CyJ0kct95Z2ZW+mMs8tPPpx+1HWtz+u7pEpc1euNorsVc49fhv7LxuY2c9DvJX6P+orZjbSbtnIT3197MPjCb2NRYbC1tqeVYK+dzy83eyp7Pen1G4J1Afgr8qcRxlVT9avXRaDWExoUaOxTFCGJTYwG4ev+qcQMpgEoWZURIdAjVbKvhZOtEcnL2Vpnzk3tsZUYGnDeh6Rrj0uKoZlutZGWkxhEQHsCEthNy2nWFEPSt15cDrxzgwMsHaOPehhn/zMB7gTdBd4KoX61+vncZjWw6ki5eXZj892TCE4w7a76Pkw+AaoqqoN5s8yY+Tj4MWDOAwIhAY4eTL5UsyoiQ6BDdN1ANfP01eSyv+u9rCwuIiird+PKjlVpiUmJwsnUqUTmHbx1GIulaq2ue73ep1YXto7Zz0u8k1hbW+G3xw8vBixuxN/LcXwjBskHLSM1I5fWtrxt1uVqfarpkERKtOrkrouqVqrP7pd3YWtoy5o8xxg4nXypZlBHZycLfHxIKWJAuMxNMZbHB2NRYJLLENYsDNw9gaWZJO888V+XN0cqtFfP7zOdE+Al2X9/NveR7D8xRlJtPNR9m9ZjFlpAt+G3x43ykcapjtha2WJpZmuRYC6V0uFVyQ5OpobGLCXQ25kPdOlsGJKYncjvhNvWr1Sc0lFzNUHlr1QqaNCmd2AqSvSBSSWsWB24eoI1Hm0JNMPdCsxeYdXBWTuf2zdibNHHN+wOZ1H4Sl+9fZkXQCpYGLqWJSxOq2FTBxsIGa3NrrC2ssTa3znndxqMNY1uO1csgNSklG4M3MmHHBDK0GTRzbVbiMpWy6efTPxOdEs3oJwq7rlzpUzWLMuDK/SuAriM0JkZXc8he3tzCPLuXW/e6TRvdnFAjR5Z+nHm5n6K7r7eaXfFrFifDTxIQHkAXry4F74yuicnRxhEHaweAfJuiAMzNzPl+4PeE/SeMeb3nUbtqbews7UjPTOde8j2uxVwj8E4g+27sY+PFjYzbMo7OyztzIepCsX8fgFtxtxi8bjDDNwzHxc6FY68dY1zrcSUqUyl70jPT6baiG69teQ1fd18G+Awo+CAjUTWLMiC7Lbt+tfpcfqAJSpCR+eCgtOvXTWOMRbboFF3NojjNUMFRwfzfP//H78G/42TrxKhmowp9rIWZBTUdanI+6jzXY68XuL+znTOTO05mcsfJ+e4jpWTVmVVM2jmJFt+3YHqX6bzf+X2sLawLHVemNpNvjn/DjL0zkEjm9Z7HpPaTsDBT/xUrIv8wfw7cPMD41uP5qPtHJj2hoKpZlAEXoi7oZvGsXI8vv3z8vnFxpjHGIltxm6H+uPgHLX9oyc6rO/mw24dcm3CNZtUL30xjLsxxsnXCxsJGbx3HQghebP4iwW8FM6LJCGbun0mrJa04eutooY4PjAik3dJ2vLvzXbrW6sr5N88zueNklSgqsPXn1iMQzO45m+qVqhs7nMdSyaIMCLwTSEPnhpw5aYdGo7vbKfdts9kcHMDe3nTGWEDxmqFWBK1g2K/DaFGjBVcnXGVm95lUsalSpPOam5lzMPQgqRmpNHVtWqRjC+Jq78rqoavZ/sJ2EtMT6bS8E+9sf4eEtLzvPEhMT+S/O/+L74++hMWHsW7YOra9sA1vR2+9xqWULZfuXWJxwGLGtx5fomba0qKSRRlwKuIUrdxaER4ONWpARkb+t81aWRlvttm8RKdEIxBUsX70j31CWgKfH/qc5t83Z2PwRgDmH53PK5tfoVftXux+aTeu9q7FOu+Zu2cAeLXlq4xrZZi+gH4+/Tj/5nkmtJvAohOLaPxdY07feXDw4baQbTT5rgnz/efzWsvXCH4rmOeaPmfSzQ1K6ZiyewpCCN5p946xQykUVf81cZFJkYTFh9HKrRXpV+DqYwZ5xsdD9erGm202L/dT7lPVtuoDE/7FpsbyzbFvWHBsAfdT7lPdvjrDfx2Os50zUclRjGg8gl+G/FKkvoCHTe4wmbORZ1k8YLFB/zBXsqrEgr4LeL7p8wz7dRiD1w3myKtHEAgm7pjIhgsbaOzSmIOvHKSzV2eDxaGULeciz/HnpT+Z1WOWSd8um5tKFiYue0TnE86tGT9at4ZFhiaTtPS8Z1sNDTWdO6FAV7PI3V+x+sxq3ToXaXE8Xf9pZnSdQVPXprT6oRWXoi8xvvV4FvVflO9ssoU1tfPUkoZeJO0827HpuU10XdGVegvrYWluSVpGGrN6zOK9Tu9hZW5VqvEopi37Dsd+Pv2MHEnhqWRh4k5FnAIg/WYrkpJg2jSYMSPvP6RmZro5okyphSM6OZpqttXI0GYw9e+pzPefTxevLnzd92taurXM2e/P5//kyK0jjGk+psw20bTxaMOZ188w59AcEtMTmd1zds7obEXJLfvLw//++R9/jPyjTNzkUOgIhRDmQABwW0o5UAjhBKwHvIEbwLNSypg8jpsIjEM3EOBHKeWCkoddcZy6c4q6VeuSEF0ZT09Ytuzf98xFBpny33/CkSN1kwuuXQszZ5Z+rHm5n3IfS3NL+q7qy57re3in7Tt82efLR6Zjrl+tfs6qcWWZTzUflg9ebuwwFBPXoJpuCv1tl7cREh1SJpqiitLBPREIzvX6fWCPlNIH2JP1+gFCiKboEkVboDkwUAihvmoVQXbntru7bgxFTAw4VNat8padKMyzKhpVq0KtWqbXwe0f5s+h0EP8NPgnFvZbaPLz9iuKId2Ov82Q9UOwMLPg52d+LhOJAgqZLIQQnsAAYGmuzYOBlVnPVwLP5HFoI8BfSpkspcwA9gNDih1tBRObGsu1mGu0rNESBwfdnFCxsRCf8GCFUDeiW7JokWTuXMmyZboE4uUFf/5pjMj/lZaRhkdlDw6+cpCXW7xs3GAUxciux1ynw7IO3Ii9wfYXtvNi8xeNHVKhFbZmsQCYAuReKaa6lDICIOsxr3sczwFdhRDVhBB2QH+gZl4nEEL4CSEChBABUaYyZaqRZS/i/oRza4YN062t/a+82/Wl1I001mrh1i0YPlw3/Yex7HpxF6dfP00bjzbGC0JRTETQnSBuxd/iiepPPLDcb1lQYLIQQgwEIqWUJ4tauJQyGPgc+BvYAZwG8lwOTEq5RErpK6X0dTGVKVONLLtzO/NWGxIT4dy5wh9rbw+1a4NGAx9/bKAAC6Gpa9MyMeBIUUrDkEZD+GHgDxy7fQzXL1wZun6osUMqtMLULDoBg4QQN4B1QE8hxCrgrhDCDSDrMTKvg6WUy6SUraSUXYH7gJqHuZBORZzC08GTlJiq2NnB3buFPzYpSXLjhm609969BgtRUZQi8mvtx+nXdYM3t4RsMXI0hVdgspBSTpNSekopvYGRwF4p5WjgTyB7pY4xwOa8jhdCuGY9egFDAROajMJ0SSk5GnaU1m6tcXWFmzehUqWH93r8LaZS6lbNS0p67G6KopSyxi6NmdppKhnajJz500xdSab7+AzoLYS4DPTOeo0Qwl0IsT3Xfr8LIS4AW4C38rq9VnlUSHQI12Ku8VTdp3KWTE1JyWvPgsckJCYat99CUZRHdavVDYC5h+caOZLCKVKykFLuk1IOzHoeLaXsJaX0yXq8n7U9XErZP9cxXaSUjaWUzaWUe/Qbfvm1NWQrAAPqDyAqCry9df0PxSElfPON/mJTFKXk+vn04/mmz7PoxCLuJd8zdjgFUhMJmqitl7fyRPUn8KrihaurboxF3gq3dvSRI3oLTVEUPZnRdQZJmiTWnjX91nmVLExQbGosB28eZKDPQEBXo5CFywn5Sk3VQ2CKouhVI+dGmAkz7iTeMXYoBTL9CUkqoJ1XdpIpMxlYX5csli4t4IBCMC/ZvHyKouhZhjaDj/d/jFZqy8REkypZmKCtl7fibONI27i9nAqxY8OG5o/ZW5B/U5QkuwNcq81nF0VRSt3V+1cZvWk0/mH+vNzi5ccu52sqVDOUicnUZrL90p/0s0pAe+oTnhzsRcH9EmVzllZFqWiklPx8+mda/NCC4Khg1g9fz0+Df8Leyt7YoRVI1SxMjH+YP/fT4hlYVXDoUntikoq2nGh+zNTXAkUxqsikSMZvHc8fF/+gi1cXVg1dhVcVL2OHVWgqWZiYbZe3YWFmTp/KlswP7oW+ag33TP/OPEUpt36/8Duvb3udhLQEvuj9BZPaTyrxAl+lTSULE7M1ZCtd3JrjWK8ttzS9sraWPGHcuaMbmNegQYmLUhSlkO6n3Oedv95hzdk1tHZrzc9Dys6U5A9TjRMm5GbsTc5GnmWg5gxc+RFXcVBvZVta6hZFUhSldGy/vJ2m3zXl1/O/8nH3jzn66tEymyhA1SxMyvbLullSBtpqAS2Jqfrr9DIzM61FkRSlvIpPi+e/O//L0sClNHVtyrYXtj2whHBZpZKFCTkYehAPW0fqWyeBhLBo/XV+ZWaCh4feilMUJQ//XP+HVza/wq34W7zf6X1mdp+JtYW1scPSC5UsTIh/6H46WMRnDY8w405GO/TVwZ2RoVujW1EU/UvWJDNt9zQWHl+Ij5MPh145RIeaHYwdll6pZGEi7ibe5Xp8OG85Zw2yk5CWkuc6UcViaQkNG+qtOEVRshy9dZQxf4zh8v3LvNP2Heb0mlMmxk0UlergNhHHbh8DoL2NJPh2Q7p+spfTl931Vr5Go6YpVxR9SstIY9ruaXT+qTNpmWnseWkPC/stLJeJAlSyMBn+Yf5YCEEzcwue/nILdV2v6LV8R0d1N5Si6EtgRCC+P/ry2eHPeKXFK5x94yw9a/c0dlgGpZqhTIT/9Z20sJKcvtaexNRKaLX6zePx8XDhgl6LVJQKJyYlhk8PfsqCYwtwsXNh6/NbGVB/gLHDKhUqWZiATG0mJ+6c5eVKEH7bnZrVbnE9qrYeSn6wc1wlC0UpnvTMdL478R2fHPiEmJQYXm7xMl/0+QInWydjh1ZqVDOUCThw8wCJmRq62oJ71XBuRdfE2tYGfd0JZWGhWw/j0iXVb6EoRSGlZFPwJhovasy7O9+llVsrAscHsnzw8gqVKEAlC5Ow5uwaKpmZM8Ae2tU7hr11ElGJLnorPzMTatbU3Q2l+i0UpXCu3L9C/zX9GfrrUGwsbPhr1F/sGr2L5jUet2RA+aWShZGlZaTx24X1DLHPxM4MrCw0/PnfQcTEV9bbOczMdBMJtm+vRnErSkFSNCl8+M+HNP2uKYdDDzO/z3yCXg+ib72+CFFxlwNQycLItl/eTmxaAqNy5YYmnhf4adwLFHZ97YJYW8OqVZCUpEZxK8rjbA3ZSpPvmvDxgY8Z2mgoF9++yLsd3sXCTHXvqmRhZGvOrcHVwoJedg9u71z/AJXtUopU1oi2G3K9kpibSxo31o3crloVtmxRo7gVJS83Ym8weN1gnl77NDYWNux9aS9rhq3BvbL+xjqVdSpZGFFcahxbLm3huTpdsHiodmtloWHB20VZfFvw2/HhmAktz7TexGs9lnP696VERcHq1fDmm/Dzz2oUt6LklpaRxuwDs2m8qDF7ru3h8yc/J+j1IHrU7mHs0ExOoZOFEMJcCBEohNia9dpJCPG3EOJy1mPVfI57VwhxXghxTgixVghho6/gy7qNwRtJy0xjVJc50OYHsHJ+4P3Rvt/i6ZaGt3fBZXXrEIeDXTzmZpkE3WxBDce7RMmOpKRAUJDuttkhQwzyayhKmSOlZMulLTRb3IwZ/8xgQP0BBL8VzJROU7AytzJ2eCapKDWLiUBwrtfvA3uklD7AnqzXDxBCeAATAF8pZVPAHFANIVnWnFtD3ap1aevRFnz8wOHBue6tNJfZ8W47vNzisbcHq4eu4eylUs3NwVpEc/ubmkzq+xU37tXm0z+m0WtYExwdISSkdH4fRSkLztw9Q+9fejNo3SDMhBk7Ru1gw4gN1KxS09ihmbRCJQshhCcwAMjdLjIYWJn1fCXwTD6HWwC2QggLwA4IL1ak5UxEQgR7r+/lhWYv/HuHhTb1kf2aeJxm/5wxJCZCWhr88QdUyVqW28ICnn9eN5VHitYZ97fD2BL4NDaWqXTvlEBKCqxcCaNGqfEVihKZFMn4LeNp+UNLTkWcYmHfhZx94yxP1XvK2KGVCYWtWSwApgDaXNuqSykjALIeXR8+SEp5G/gCCAUigDgp5a6SBFxerD+/Hq3U8kKzF/7dWPfVvHe+vQWijgIweDDExuoG2aWl6V7Xrg0HjjoQd/MCS+acxMzCgtqu17GKO0rPnjBwoBpfoVRcaRlpzD08F59vfFgetJx32r7DlQlXeKfdO1iaWxo7vDKjwGQhhBgIREopTxa18Kx+jMFAbcAdsBdCjM5nXz8hRIAQIiAqKqqopypzVp9dTSu3VjR0ztXj7OOn67tweHjpRQmR+3JeBQdD9+66GsXUqXDlCmi1gEsHwtPaYWOWgCd/wt5eEHWUWrXU+Aql4pFS8vuF32n8XWOm7p5K11pdOffGORb0XVDhRl/rQ2FqFp2AQUKIG8A6oKcQYhVwVwjhBpD1GJnHsU8C16WUUVJKDbAR6JjXSaSUS6SUvlJKXxcX/Y1eNkUh0SEEhAcwqtmoR9/08YN2S0Hkuq9bCLCqBuimGn/qKV2CSEyEmzd1NY2hQ3Xv3b12g5gkR55ttxa06RC5j9BQNb5CqVhORZyi+8ruDN8wHDtLO3aN3sWW57fQwLmBsUMrswpMFlLKaVJKTymlN7rO6b1SytHAn8CYrN3GAJvzODwUaC+EsBO6hvlePNhJXiGtObsGgeC5Js/lvUPkPl07UzaphVOTIOooq1fDrVu6moKZ2b+d3ps3g5MTLP69M65VIvnz1DNoMq3Zd3mQGl+hVBhh8WG8/MfL+C7x5ULUBRYPWEzg+EB61+1t7NDKvJKMs/gM6C2EuAz0znqNEMJdCLEdQEp5DPgNOAWczTrfkhJFXMZJKVl9djU9avfAwyGfr/uu3cHcin8nEpSQmYrm8ir+858Hd01P11U8ALp2heBtq9gzvQ9/ne6Ls184b06trcZXKOVefFo80/dMx+cbH9aeW8t/O/yXK+9c4XXf19Xoaz0RUupnSgl98vX1lQEBAcYOwyCO3z5Ou6XtWDZoGWNbjs1/x6ijcP1nuLoMpAaAg5e60XfeHpJTzBFC17EtJVy/rrt9NjNTYiYyAYFH1dt8+/IEBo1tB02mlc4vpyilTJOp4cdTPzJz30yikqN4odkLzO45G29Hb2OHVuqEECellL6GKl+N4C5la86uwcrciqGNhj5+R5cO0HYxOP+76Hv4/epUqxwN6JJEUtK/NYbMTLK2C6SEW/drMnj+RhZve9YQv4aiGJWUks0XN9NscTPe2v4WjV0ac2LcCVYPXV0hE0VpUPWzUpShzWDduXUMrD8QRxvHwh2U+e/YC/eq4cQn2mTVIuDuXdix48Hd5QP5X/Dm1LpQGd54o8ThK4pJOHH7BJP/nsyBmwdoUK0Bm0du5un6T1foGWFLg6pZlKK91/dyN+lu3ndB5ad695yn7eodw84qAWuLVMzNdENe8m9FlDmPEyeqQXlK2Xcj9gajNo6i7dK2BEcF813/7zj7xlkGNRikEkUpUMmiFK05u4Yq1lXo79O/cAdEHYVLX+e8tLLQsP29AWi1EjOh4fFTmAtAIpDY26tBeUrZFZsay5S/p9Dg2wZsDN7I9C7TuTLhCm+0eUMNqitFKlmUkhRNChuDNzKs0TBsLAo5l2LkPt1YiVxaeJ8mYJYvHXz8sTDPfOiAh79dCSQCKys1KE8pe9Iz0/na/2vqLqzLF0e+4Pmmz3P5ncvM6jkLB2sHY4dX4ag+i1KyNWQrCekJjHqiCE1Qrt3BzAq0aQ9sbuJ5gf3/e5I3dt3k+xUPz7evq1HkJjPT8fBQM2kqZUNkUiTrzq1j4bGFXI25ypN1nmRe73m0qNHC2KFVaKpmUUpWn12NWyU3utXqVviDXDpAr3+gSos83hRYJwagSwyPv/05KtqSdu2KEKyiGIGUkh9P/kjdhXWZuGMijjaOOeteq0RhfCpZlIKYlBi2X97O802fx9zMvOgFJObROy01NHPclHsDeSUOM5FJdecUjh0r+mkVpbSExYfRf01//Lb60ca9DWffOEuAX0CFX/falKhmqFLgH+aPRqthcMPBRT84ch9kpuX5VorGLs/tuXVpcJAM23rcvl3wvopS2uJS45h3ZB5f+X8FwLf9vuWNNm9gJtT3WFOjkkUpCI0LBaBu1bpFP9i1Owgz3fxQD6lqF4OFWQYZ2rz/GS3MNJy84Uu3Lho1kaBiUtIy0vg+4Hs+OfAJ0SnRjGw6kk97fkrtqrWNHZqSD5W+S8HNuJtYmFlQo1KNoh/s0gF8F4F4tPnKy/kWVhZ51zoAMrSWjB2dwP6jTmoiQcUkaKWW1WdW03BRQybtnESLGi0IGBfA2mFrVaIwcapmUQpuxt2kpkPN4vVXgG7a8phAuPL9A5vb1TuGlUU6yen2+RwoWPOHu5pIUDEJu67uYuruqQTdCaJFjRbsHL2TPnX7GDsspZBUzaIU3Iy9iVcVr5IVUvsldEuY/8vKQkOfZjsf2lGLrpNb4OgIzzwDQ4aU7NSKUhInw0/S+5fePLXqKWJTY1k1ZBUn/U6qRFHGqJpFKQiNC6VH7R56KOnBO500GRbsPtfroX10+V8I3Y/qq1CM5X7Kfab8PYVlgcuoZluNBU8t4HXf17G2sDZ2aEoxqGRhYJpMDbcTbuPlUMKaReQ+Hk4W/lfak5pum+fuUkJ8vFr0SCl92Wu2/Gfnf7ifcp/JHSYzo+sMqthUMXZoSgmoZGFgtxNuo5VaajnWKllBrt11S61mrW0BEB7jThX7OJJjbdE1Ueman7J17Kj6KpTSdeX+Fd7Y9ga7r+2mnUc7/n7xb5rXaG7ssBQ9UH0WBpZ922ytKiVMFi4doGrLBza5Vw0nJd2W7ASRPRMtgJ1tOomJJTulohRWemY6sw/Mpul3TTl++ziL+i/i8NjDKlGUI6pmYWD7buwDwKeaT8kKijoKZg9OQNiu3jEc7WKJTa4KgJV5KilaW8CMarYRkGYHuJTsvIpSgEOhhxi/dTwXoi4wovEIFvRdgHvlh+csU8o6VbMwoPi0eBb4L2BQg0ElW70r6ijs7QX3Dj6w2cpCw0fDZ2IudDWKFI0dHlXDaeZ5lrAYTzr6HC1B9IryeDEpMYzfMp4uP3UhMT2RLc9v4dcRv6pEUU6pmoUBLTq+iJjUGP6v6/+VrKCcKT8enTDwXrwLtlbJJKVXwsIsg+hEJxJSHDATkrdfTynZeRUlHxuDN/Lmtje5l3yP/3b4LzO7z6SSVSVjh6UYkKpZGEhieiLz/efT36c/vu4lXEPdtbvuPtiHaDIs+PKv/4AQBHzSikbuwaRq7NBKM4a1+Y2GrUt4B5aiPCQyKZJnNzzLsF+H4V7ZnRPjTvBFny9UoqgAVLIwkO8Dvude8r2S1ypA17ldrdMjm/2vtCcj0wJXh0h2ne1LwCxfejTeS3qmFR8Nn5l1u62ilJyUkjVn19B4UWM2X9rM7J6zOfbaMVq6tSz4YKVcUMnCAJI1ycw7Mo/edXrT3rO9fgq1dnpkU3iMO17Ot/jzv4P463Q/nF+/x5nQ5nRtcICG7pfAqpp+zq1UaOEJ4Tyz/hlGbRxFXae6nPI7xQddPlBLmlYwqs/CAJacXEJkUiT/6/Y//RVq++gkhO5Vw7kVXZNG7sHs/7+egJYXvl1NfbcQwAzSo/V3fqVC+uvyX4zeNFr3Baj3PN5t/27x5zhTyrRC1yyEEOZCiEAhxNas105CiL+FEJezHqvmcUwDIURQrp94IcQkPcZvclI0KXx++HN6ePegs1dn/RVc9dHqfrt6x7C3TmLu1iloMszYd6EbWwKfZmSH9WBurevrUJRiyNBmMH3PdPqv6Y+ngydB44OY3HGyShQVWFGaoSYCwblevw/skVL6AHuyXj9ASnlJStlCStkCaA0kA5se3q88WRa4jDuJd/Rbq4A8awlWFpoHmqDe/Ok7fn79JRo2dYCee3R9HYpSRHcS79D7l958euhTxrYYi/+r/jRwbmDssBQjK1QzlBDCExgAzAb+k7V5MNA96/lKYB8w9THF9AKuSilvFifQsuLrY1/TqWanoq21XRiu3UFYPjDdB0ATzwvs/79eWQskZYCZFbT+RyUKpVj239jPyN9HEpcax0+Df+LlFi8bOyTFRBS2ZrEAmIJu/uts1aWUEQBZj64FlDESWJvfm0IIPyFEgBAiICoqqpBhmZ57yfdo7dZa/+sGu3SAuq8+ut3OG3ofhCf3Q/PZ0EslCqXotFLLnINz6PlzTxysHTj22jGVKJQHFJgshBADgUgp5cninkQIYQUMAjbkt4+UcomU0ldK6evioqaoyFMea1qQfEv36NIBmkxTiUIpsvsp9xm0dhAf7P2A4Y2Hc2LcCZpVb2bssBQTU5iaRSdgkBDiBrAO6CmEWAXcFUK4AWQ9Rj6mjH7AKSnl3RLGW7G5dIA235F7ZlmQajyFUmzHbx+n1Q+t2HV1F9/0+4Z1w9bhYO1g7LAUE1RgspBSTpNSekopvdE1Je2VUo4G/gTGZO02Btj8mGKe5zFNUEoR+PhBm+9105Vjpu56UoolU5vJnINz6LRcN9jz0NhDvN32bf03nyrlRknGWXwG/CqEeBUIBUYACCHcgaVSyv5Zr+2A3sD4EsaqZPPxA8dmuhqFa3fV9KQUyY3YG7y46UUOhR7i2SbPsnjAYpxsHx30qSi5FSlZSCn3obvrCSllNLo7nB7eJxzon+t1MqCGEuubSweVJJQikVKyImgFE3dMRAjBL0N+YVSzUao2oRSKGsGtKBVAREIEflv92Bqyla61urLymZUlmzZfqXBUslCUckxKybpz63hr+1ukZKTw1VNfMaHdBMyEmhZOKRqVLBSlnIpKiuLN7W/y24XfaO/ZnhWDV6iR2EqxqWShKOXQpuBNjN86nri0OOb0msPkjpOxMFP/3ZXiU1ePopQjMSkxTNgxgVVnVtGyRkv2DtlLU9emxg5LKQdUslCUckBKybbL2xi/dTyRSZHM7DZTrTmh6JVKFnoUmxpLQloCVWyqGDsUpQI5GX6SKbunsPf6Xpq4NGHL81to5dbK2GEp5YxKFnq06+ouMmUmT9V9ytihKBXAtZhrTN87nXXn1uFs58zXfb9mfOvxWFtYGzs0pRxSyaKYToaf5MitIzmvhRD8duE3nGyd9LeUqqLkISopilkHZrE4YDEWZhZM7zKdKZ2mqDmdFINSyaKQkjXJ7L2+F02mhqjkKCb8NYG0zLRH9vNr5adWE1MMIiIhggX+C1gcsJgkTRKvtnyVmd1n4l7Z3dihKRVAhU0WF+9d5N2d75KpzaS9Z3s6eHagvWd7qto+sjos5yPP8+xvz3Ih6kLOtiYuTdj2wjbsrewBXQejROJs51xqv4NSMVy5f4V5h+ex4vQKMrQZjGg8gg+7fUgjl0bGDk2pQCpkstBkahixYQTXY65Tz6kesw/ORit16zo1cm5EB88OOFg7sOvaLmo71mbv9b04WDuw8dmN1KlaB4AGzg2wsbAx5q+hlHOnIk7x+eHP+e3Cb1iaWTK2xVgmd5xMXae6xg5NqYAqZLL4+tjXnIs8x+aRmxnUYBCJ6Ykcv32co7eOcjTsKJsvbSY+LZ6utboSeCeQ7t7dWT54OTUq1TB26Eo5l6xJ5rcLv7H01FIOhh7EwdqBKR2nMLH9RHX9KUYlpJTGjuERvr6+MiAgwCBlJ2uScf/SnU5endj2wrY895FSkp6Zru4qUUpNYEQgS08tZfXZ1cSlxeHj5MO4VuPwa+2nbsVWCkUIcVJK6Wuo8itczeLb498SlxbHB50/yHcfIYRKFIrBpWaksv7cer4L+I7jt49jY2HD8MbDea3la3St1VVNHa6YlAqVLDK0GXx68FP61etHJ69Oxg5HqaCux1xnccBilgcuJzolmobODVnYdyGjnxid5w0WimIKKlSyiEuNIy4tjt51ehs7FKWC0UotO67s4LsT37H98nbMhBnPNHyGN9u8SQ/vHqoWoZi8CpUsbsTeAFCLvih5StGksO7cOlaeXolXFS9ebvEy3b27l2jth9SMVH4+/TPzjszjyv0rVLevzoyuM/Br7Yeng6ceo1cUw6pQyUIt+KLk5/Sd0wz7dRhXY67SoFoDgu4E8cuZX2js0piDrxws8hrV8WnxfB/wPV/5f8WdxDu0cW/D2mFrGdpoKFbmVgb6LRTFcCpUsshuD45JjTFyJIopWRG0gje2vYGTrRM7Ru2gT90+pGak8uv5X3n1z1eZvmc6iwcuLlRZdxPv8vWxr/nuxHc5TZ6rh65WTU1KmVehkkX2wLvsR6ViC44KZtqeaWy+tJmetXuydthaXO1dAbC1tGVMizEE3glk4bGFjG05ljYebR44PjY1lusx1wmLD+NO4h1ORpxk5emVpGWkMbzxcKZ2mkpr99bG+NUURe8qVLK4eO8iAI1dGhs5EsWYbsXdYtaBWSwNXIq9pT1zes3hvY7v5Tmn10fdP2L9+fWM/XMsvWr34kbsjZyfuLS4B/a1MrdiTPMxvNfxPXyq+ZTWr6MopaJCJYu4VN1/7qK2Pyvlw624W3y8/2NWnF6BQPB2m7eZ0XUGLvYu+R5TxaYKX/f9mud+e47rMdfxdvTG29GbLl5dcp7XrFITt0puVK9UXfVHKOVWoZOFEMIcCABuSykHCiGcgPWAN3ADeFZK+UhngBDCEVgKNAUkMFZKebTEkRdDfFo8AJWsKhnsHJnaTHx/9OWlJ17i3Q7vGuw8SuFFJkXy6cFPWRyg63fwa+XHlE5TqOVYq1DHP9vkWQb4DMDO0k71OygVVlFuD5oIBOd6/T6wR0rpA+zJep2Xr4EdUsqGQPOHyihV2bN0/nX5L4Od4/jt4wTdCeI/u/5jsHMohRObGsuMvTOo83Udvjn+DaObjSbk7RAWDVhU6ESRzd7KXiUKpUIrVLIQQngCA9DVELINBlZmPV8JPJPHcQ5AV2AZgJQyXUoZW/xwS6aLVxdau7Vm0YlFBjvH5kubc56b4rxb5Z1WatlzbQ8vbXoJj/kezD44m4H1B3LhzQssG7ysyElCURSdwjZDLQCmAJVzbasupYwAkFJGCCFc8ziuDhAF/CSEaA6cBCZKKZMe3lEI4Qf4AXh5eRX6FygKIQT1nOpx+NZhg5QP8OelP3OeRyVH5dxdoxhWSHQIK4NW8suZX7gVfwsHawdGNRvFm23epEWNFsYOT1HKvAJrFkKIgUCklPJkMcq3AFoBi6WULYEk8mmuklIukVL6Sil9XVzy73AsicT0RLaGbDXYGtmXoy8TfC84p/xrMdcMch5FJzY1lh8CfqDDsg40+LYBnx3+jCauTVg7bC13/nuHJU8vUYlCUfSkMDWLTsAgIUR/wAZwEEKsAu4KIdyyahVuQGQex4YBYVLKY1mvfyP/vg2D++3CbyRpkni5xcsGKT+7VjGp/SR2Xt3JtZhraj1uPcvQZrDr6i5Wnl7J5oubSctMo4lLE+Y+OZdRT4xSS4wqioEUmCyklNOAaQBCiO7AZCnlaCHEPGAM8FnW4+Y8jr0jhLglhGggpbwE9AIuPLxfaVl9djX1nOrRqab+Z5zN1Gay4vQKmldvTrda3YCyU7PI0GYgECa9dvjZu2dZeXolq8+u5k7iHarZVsOvtR9jmo+hlVsr1fmsKAZWknEWnwG/CiFeBUKBEQBCCHdgqZSyf9Z+7wCrhRBWwDXglRKcs0Qu3rtIr9q9DPKH5YeTP3Au8hwbRmzA1tIWt0puJpsspJRci7nGX1f+4q8rf7H/xn7sLO34oMsHTGw30WT+8EYlRbHm7BpWnl5J4J1ALMwsGFh/IGOaj6G/T381pkFRSlGRkoWUch+wL+t5NLqawsP7hAP9c70OAgy2elNhaaWWO4l3DNJMcS/5HjP2zqBn7Z4MazQMgDpV65hcsgiOCmbukblsC9lGVHIUAHWr1mVM8zEE3Q3i3Z3v4u3oTUxKDDdibxCdEk2GNoNaVWpRu2ptPCp7YGdph62lLbYWtrjYuxhkzEp4Qjgz983kp6CfyNBm0NqtNQv7LuT5Zs/jbOes9/MpilKwCjOCOyopigxtBm6V3PRe9oy9M4hPi2dh34U538rrVK3Dvhv79H6u4sj+47sscBm2FrYMbTSUdh7t6FO3T860FKvOrOLIrSMMWT8EAIHA0cYRM2FGdEp0nuWaC3PaeLShp3dPetbuSceaHbG1tC12nPFp8cw7PI/5/vPRZGoY33o8b/i+QRPXJsUuU1EU/agwyeJqzFUA6jrV1Wu5gRGBLDm5hAntJjzwR82rihdh8WFIKY3WrBOfFs/cw3OZf3Q+GdoM3mn7DjO6zsjz2/nA+gP56qmv8KriRVPXptSpWgcLM93lkZieyI3YG0QkRJCSkUKKJoWUjBSu3L/CPzf+4fPDn/PpoU+xMrfC192X+LR4bsffxsLMAmsLa6zMrbAyt8LaPNfzh7Zbmlvy99W/iUqO4rkmzzG752y9/1spilJ8FSZZhESHAFC/Wn29lSml5J2/3sHZzpmZ3Wc+8J5WajE3MzdKotBKLT+e/JEZ/8zgXvI9RjYdyeyes6lTtU6+xzjaODKp/aQ836tkVYmmrk1p6to0z/cT0hI4FHqIvdf3cjTsKLUda9PVqyuZMpP0zHTSM9NJy0z793lGGmkZaSSkJTywvZVbKz7p8ckjs7sqimJ8FSZZXI6+jIWZhV5XyVt9djWHbx1m2aBlONo4PvBeiiYFW4uSNcnsvLITt8pudPbqXOjjbsTeYPTG0Ry+dZhutbrxRZ8v8HU3bJdRZevK9PPpRz+ffgY9j6IoxlNhkkXI/ZAHmlZKKiEtgSl/T6GNe5s8x22kZqQWuf0+NC6ULZe28GfIn/xz/R80Wg2NnBtx4a3C3W28+eJmXt78MlqpZcXgFbzU/CWTubNJUZSyreIki+gQvTZBzTowi4jECDY9tynP5VpTMlKwsbB5bBlSSoLvBbMxeCObLm7iVMQpADwdPNFoNQB09+5eYCyaTA3v736f+f7zaeXWig0jNjy2yUlRFKWoynWyWHN2Dbuv7aZP3T6ERIfQu05vvZR76d4lvvL/ildavEI7z3Z57pOSkXczlJSSE+En2BS8iY0XN+b0pbT3bM/nT37O4AaDmX90PksDl/Jx94+Z2H7iY2MJjQvlud+ewz/Mn7favMWXfb7E2sK65L+koihKLuU2Wey/sZ8xf4xBSslPQT8B8FLzl0pcrpSSSTsnYWtpy5xec/LdL0WTktMMlaHN4MDNA2wK3sQfl/4gLD4Mc2FOj9o9mNhuIoMbDMbDwSPn2KC7QfSs3ZPpXafnW35oXCi/nv+VOYfmoMnUsH74ep5t8myJfz9FUZS8lMtkERoXyogNI6jnVI9Drxwi8E4giemJPFH9iRKXvSVkCzuu7GB+n/lUr1Q93/3i0+IJuhPEK5tfYculLUSnRGNjYUPfen2Z3VM3bXZ+K/bFpMRQ27H2I9tvxd3itwu/8euFX/EP8wegs1dnlg9arpbxVBTFoMpdskjPTOeZdc+QlpnGH8/9QTW7ajxZ50m9lJ2akcq7O9+lkXMj3m779mP3ze5I3xS8iYH1BzK00VCeqvsU9lb2BZ4nJjWGqjZVc17vu7GPGXtn5Eyt3rJGS+b0msOIxiPUWARFUUpFuUsWR24dIfBOIL8M+YUGzg30WvaXR77kWsw1/n7xbyzNLR+776L+i7gVf4uutboWaQ4jKSUxKTFUta1KfFo8U/+eyvcnv6e2Y21m9ZjFiCYj9NpRryiKUhjlLllk31HUp24fvZZ7PeY6nx76lKGNhhaqptLAuUGxklVieiKZMpMT4Sdo+l1Tbifc5r8d/svHPT7GztKuOKEriqKUWLlLFpejL+Nk66TXFeq0UsvLm1/GXJjz1VNf6a3cvCSkJwCw+9puGjo35PDYw2pNDEVRjK7cJYvs5hut1OY5/qE4Fh5byIGbB1g+aDleVQyz5Gu2GpVq8EmPT7A0s2Ri+4kFjtVQFEUpDeUuWbhVciNDm8G95Ht6qV1cvHeRaXumMbD+QIOtsJebmTBjRtcZBj+PoihKUejnq7cJcausm4I8IiGixGVlaDMY88cY7CztWDJwiZo6Q1GUCqvcJYvsxY0iEkueLOYensvx28dZPGBxThJSFEWpiMpdsshe3KikNYtVZ1Yxc99MnmvynBoZrShKhVf+kkV2M1QxaxYpmhT8tvjx4qYXae/Znu8GfKfP8BRFUcqkctfBvfPKTgAqW1Uu8rFX7l9hxIYRBN0JYlrnaXzc42O9TWmuKIpSlpWrv4QpmhTGbRlHyxotGe87vkjH/nbhN8ZuHouFmQVbn9/KgPoDDBSloihK2VOuksWOKzuISo5i9dDVhZ5iIz0znfd2vcfC4wtp59GO9cPXU8uxloEjVRRFKVvKVbLYcGEDznbO9Kjdo1D734y9ybO/Pcvx28eZ1G4Sn/f+vEjzOCmKolQUhU4WQghzIAC4LaUcKIRwAtYD3sAN4FkpZUwex90AEoBMIENKabAFof3D/OlVu1eh+hm2hmzlpU0vkSkz+f3Z3xnaaKihwlIURSnzinI31EQgONfr94E9UkofYE/W6/z0kFK2MGSiAEjWJFPFuspj99Fkapj691SeXvs03o7enPI7pRKFoihKAQqVLIQQnsAAYGmuzYOBlVnPVwLP6DWyYkjNSH1sM1JwVDA9f+7J3CNzeb316xx59YhaD0JRFKUQCtsMtQCYAuS+H7W6lDICQEoZIYTIbyImCewSQkjgBynlkrx2EkL4AX4AXl7Fm6yvgXMDAiICHth26d4lNlzYwK/nf+Vs5FnsLe1ZPXQ1LzR7oVjnUBRFqYgKTBZCiIFApJTypBCiezHO0UlKGZ6VTP4WQlyUUh54eKesJLIEwNfXVxbjPPSr14+P93+Mf5g/e67t4dcLv3Lm7hldEDU78XXfrxnReISaukNRFKWIClOz6AQMEkL0B2wAByHEKuCuEMItq1bhBkTmdbCUMjzrMVIIsQloCzySLPShX71+fLT/Izos66ALvGYnFjy1gGGNh+Hp4GmIUyqKolQIBSYLKeU0YBpAVs1ispRytBBiHjAG+CzrcfPDxwoh7AEzKWVC1vM+wMd6i/4hbTza8H6n96leqTrDGw9XCUJRFEVPSjLO4jPgVyHEq0AoMAJACOEOLJVS9geqA5uypva2ANZIKXeULOT8mQkz5jw5x1DFK4qiVFhFShZSyn3Avqzn0UCvPPYJB/pnPb8GNC9pkIqiKIpxlbtZZxVFURT9U8lCURRFKZBKFoqiKEqBVLJQFEVRCqSShaIoilIglSwURVGUAqlkoSiKohRISFmsaZgMSggRBdwsQRHOwD09hWMIphyfKccGph2fKccGph2fKccGZSM+eymli6FOYJLJoqSEEAGGXjujJEw5PlOODUw7PlOODUw7PlOODVR8oJqhFEVRlEJQyUJRFEUpUHlNFnkusGRCTDk+U44NTDs+U44NTDs+U44NVHzls89CURRF0a/yWrNQFEVR9EglC0VRFKVAZSpZCCHWCyGCsn5uCCGCHnrfSwiRKISYnM/x84QQF4UQZ4QQm4QQjlnbvYUQKbnK/t4IsTkJIf4WQlzOeqya671pQogrQohLQoinihrb4+ITQrTNtf20EGJIEY832GdXhNhmCiFu59q3f673TOGzK/XrrgixGeu66y2EOCmEOJv12LOIx5f4s9NTfAa79vQQm36vOyllmfwBvgT+99C234EN6JZ+zeuYPoBF1vPPgc+znnsD54wc21zg/azn7+eKrTFwGrAGagNXAXN9xQfY5fpMstdStyjC8Qb77AobGzAzr8/VVD47Y1x3RYjNWNddS8A963lT4HYRj9frZ1fc+Err2itmbHq97spUzSKbEEIAzwJrc217BrgGnM/vOCnlLillRtZLf0Dvi3QXNzZgMLAy6/lK4Jlc29dJKdOklNeBK0BbfcUnpUzO9ZnYAI+94yGv309fShpbHkziszPGdVeEz85Y112g1K2qCbr/FzZCCOvCHq9vJY0vD3r7/Iobm76vuzKZLIAuwF0p5WUAIYQ9MBX4qAhljAX+yvW6thAiUAixXwjRxQixVZdSRgBkPbpmbfcAbuXaLyxrm17iy4qxnRDiPHAWeD3XBVao4zHQZ1fE2N7Oqm4vz9WUYmqfHZTSdVeE2Ix23eUyDAiUUqYV8Xh9fXYljc/Q115JPzvQw3VXpDW4S4MQYjdQI4+3pkspN2c9f54Hv2F8BHwlpUzUJeECzzEdyABWZ22KALyklNFCiNbAH0KIJlLK+NKOLa9w89iW57fEYsaHlPIY0EQI0QhYKYT4S0qZmk88Dx9vyM+usLEtBj5B97l8gq7KPhYT++xK+bor6r/rI+HmsU2vn13WsU3QNZH0KSCeYl13pRBfia690vjsinvdPRq9Htv8SuMHXYK7C3jm2nYQuJH1EwvcB97O5/gxwFHA7jHn2Af4lmZswCXALeu5G3Ap6/k0YFqu/XYCHfT12eWxzz/5/e6FPF5vn11RYsu1jzdZ7bEm9tmV6nVX2NiMed2haxYJATrp4fcr1menj/gMee3p4bPT23VX5A/W2D9AX2D/Y96fSf6dyH2BC4DLQ9tdyOp8AuoAtwGnUo5tHg92NM7Net6EBzvKrlHMjrK84ssqM7sTrBYQDjgX4XiDfXaFjY2sP3ZZz99F11ZsMp+dMa67IsRmrOvOMav8YcU8Xi+fXUnjM/S1V8LY9HrdFfmDNfYPsAJd+2t+788k1x9kYClZWRNdJ9MtICjr5/us7cPQdRSdBk4BTxshtmrAHuBy1qNTrv2mo7ub4hLQT5+fHfBi1u8elPW7P5NXfI853mCfXWFjA35B1y5/Bvjzof/ARv/sjHHdFSE2Y113M4CkXJ9JEOBa2tddSeMz9LVXwtj0et2p6T4URVGUApXVu6EURVGUUqSShaIoilIglSwURVGUAqlkoSiKohRIJQtFURSlQCpZKIqiKAVSyUJRFEUp0P8DqJ7IUxJr4G8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for NYup\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 0.199 TN-noMemphis seats out of 8\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the NYC-LI map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  777126.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 5.358 4.976\n",
      "fractional expected GOP seats =  3.849  out of  10 seats for NYup\n",
      "simpler expected GOP seats =  3.6411  out of  10 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOTE - I DID NOT PULL MEMPHIS BACK INTO THIS ANALYSIS. (can be pieced together if needed)\n",
    "#I manually corrected responsiveness and stateGOP2 for the added Dem district"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
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